Event detection, confirmation and publication system that integrates sensor data and social media

ABSTRACT

Enables integration of sensor data with other information on servers such as social media sites to detect, confirm and/or publish events. Sensors may measure values such as motion, temperature, humidity, wind, pressure, elevation, light, sound, or heart rate, etc. Sensor data and event tags may be utilized to curate text, images, video, sound and post the results to social networks, for example in a dedicated feed. Event tags generated by the system may represent for example activity types, players, performance levels, or scoring results. The system may analyze social media postings to confirm or augment event tags. Users may filter and analyze saved events based on the assigned tags. The system may create highlight and fail reels filtered by metrics and by tags. Recommendations may be provided to a user based on analysis of sensor data and other information; recommendations may include for example recommended friends, purchases, or activities.

This application is a continuation in part of U.S. Utility patentapplication Ser. No. 15/184,949 filed 16 Jun. 2016, which is acontinuation in part of U.S. Utility patent application Ser. No.14/801,631 filed 16 Jul. 2015, which is a continuation in part of U.S.Utility patent application Ser. No. 14/549,422 filed 20 Nov. 2014, whichis a continuation in part of U.S. Utility patent application Ser. No.14/257,959 filed 21 Apr. 2014, which continuation-in-part of U.S.Utility patent application Ser. No. 13/914,525, filed 10 Jun. 2013, nowU.S. Pat. No. 8,702,516, which is a continuation in part of U.S. Utilitypatent application Ser. No. 13/679,879 filed 16 Nov. 2012, which is acontinuation-in-part of U.S. Utility patent application Ser. No.13/298,158 filed 16 Nov. 2011, which is a continuation-in-part of U.S.Utility patent application Ser. No. 13/267,784 filed 6 Oct. 2011, whichis a continuation-in-part of U.S. Utility patent application Ser. No.13/219,525 filed 26 Aug. 2011, which is a continuation-in-part of U.S.Utility patent application Ser. No. 13/191,309 filed 26 Jul. 2011, whichis a continuation-in-part of U.S. Utility patent application Ser. No.13/048,850 filed 15 Mar. 2011, which is a continuation-in-part of U.S.Utility patent application Ser. No. 12/901,806 filed 11 Oct. 2010, whichis a continuation-in-part of U.S. Utility patent application Ser. No.12/868,882 filed 26 Aug. 2010, the specifications of which are herebyincorporated herein by reference.

This application is a continuation in part of U.S. Utility patentapplication Ser. No. 15/184,949 filed 16 Jun. 2016, which is acontinuation in part of U.S. Utility patent application Ser. No.14/801,631 filed 16 Jul. 2015, which is also a continuation in part ofU.S. Utility patent application Ser. No. 13/757,029, filed 1 Feb. 2013,the specifications of which are hereby incorporated herein by reference.

BACKGROUND OF THE INVENTION

Field of the Invention

One or more embodiments pertain to the field of event detection andtagging through use of sensors and media to detect events found inmotion capture data, and/or media such as posts in a social media siteand/or other sensors including but not limited to one or more ofinertial, i.e., that detect orientation, position, velocity,acceleration, angular velocity, angular acceleration, or physicalsensors, environmental sensors, chemical sensors and physiologicalsensors, i.e., electromagnetic field, temperature, humidity, wind,pressure, elevation, light, sound, heart rate, etc. Embodiments alsoenable motion capture data analysis and displaying information based onevents recognized within the motion capture data or within motionanalysis data associated with a user, or piece of equipment and/or basedon previous motion analysis data from the user or other user(s) and/orpiece of equipment. More particularly, but not by way of limitation, oneor more embodiments enable a system that enables intelligentsynchronization and transfer of curated event videos, i.e., generallyconcise event videos, synchronized with motion data from motion capturesensor(s) coupled with a user or piece of equipment. Greatly savesstorage and increases upload speed by only saving or sending/obtainingor transferring relevant portions of media, e.g., uploading event videosinstead of larger text, audio, image, video information with unwantedinformation. Creates highlight reels filtered by metrics and can sort bymetric. Integrates with multiple sensors to save event data even ifother sensors do not detect the event. Events may be correlated and/orotherwise synchronized with image(s) or video, as the events happen orat a later time based on location and/or time of the event or both, forexample on a mobile device, which may include a camera, glasses withcamera(s) and/or having a processor, mobile devices with camera(s), oron a remote server, and as captured from internal/external camera(s) ornanny cam, for example to enable saving video of the event, such as thefirst steps of a child, violent shaking events, sporting, military orother motion events including concussions, or falling events associatedwith an elderly person and for example discarding non-event relatedvideo data, to greatly reduce storage requirements for event videos. Thesystem may automatically generate tags for events based on analysis ofsensor data; tags may also be generated based on analysis of socialmedia site postings describing the event. The system may use thecombination of sensor data and media for example from social media sitesto not only detect, confirm and publish events and curate media toprovide concise versions of the events, but also determine whether anevent is valid or invalid or represents fake news. One or moreembodiments may be utilized to analyze multiple social media posts, orthreads that are unknown across “friends” to determine events and/orprovide emergency notifications, for example to flash all mobile devicescreens in case of a local emergency or terrorist attack, detected,confirmed and published by an embodiment of the invention.

Description of the Related Art

Existing systems do not utilize sensor data such as inertial data, i.e.,motion capture data, including one or more of orientation, position,velocity, acceleration, angular velocity, angular acceleration, or othersensors such as physical, environmental, chemical and physiologicalsensors, i.e., electromagnetic field, temperature, humidity, wind,pressure, elevation, light, sound, heart rate, etc., to detect, confirmevents, or public, i.e., post events, or differentiate similar types ofmotion events to determine the type of equipment or activity or qualityof the event, such as how proficient a user is at a certain activity.Known systems do not curate or otherwise provide concise versions oftext, images, (or 360 images), video, (or 360 video), sound or virtualreality for events and post the results to social networks using motionor other sensor data, for example in a dedicated feed. Known systems donot post or filter to social media sites for example using any otherfilter besides location and time and the text in the social media postsfor example. There are no known systems that also use motion or othersensor data to define and event, eliminate false positive events, posttrue events, and/or correlate the events with social media to confirmthe events, or post the events in a particular channel for example.Known systems do not use the combination of sensor data and media forexample from social media sites to confirm events and do not curatemedia to provide concise versions of the events, and also do notdetermine whether an event is valid or invalid or represents fake news.Known systems do not analyze multiple social media posts, or threadsthat are unknown across “friends” to determine events, for example incombination with any sensor data and do not provide emergencynotifications for example flash all screens, such as smart glassesscreens or mobile device screens, etc., in case of a local emergency orterrorist attack.

Existing motion capture systems process and potentially store enormousamounts of data with respect to the actual events of interest. Forexample, known systems capture accelerometer data from sensors coupledto a user or piece of equipment and analyze or monitor movement. Thesesystems do not intelligently confirm events using multiple disparatetypes of sensors or social media or other non-sensor based information,including postings to determine whether an event has actually occurred,or not, such as fake news, or what type of equipment or what type ofactivity has occurred. In these scenarios, thousands or millions ofmotion capture samples are associated with the user at rest or notmoving in a manner that is related to a particular event that theexisting systems are attempting to analyze. For example, if monitoring afootball player, a large amount of motion data is not related to aconcussion event, for a baby, a large amount of motion data is notrelated in general to a shaking event or non-motion event such as suddeninfant death syndrome (SIDS), for a golfer, a large amount of motiondata captured by a sensor mounted on the player's golf club is of lowacceleration value, e.g., associated with the player standing or waitingfor a play or otherwise not moving or accelerating in a manner ofinterest. Hence, capturing, transferring and storing non-event relateddata increases requirements for power, bandwidth and memory.

In addition, video capture of a user performing some type of motion mayinclude even larger amounts of data, much of which has nothing to dowith an actual event, such as a swing of a baseball bat or home run.There are no known systems that automatically curate or otherwise trimvideo, e.g., save event related video or even discard non-event relatedvideo, for example by uploading for example only the pertinent eventvideo as determined by a sensor and/or motion capture sensor, withoutuploading the entire raw videos, to generate smaller media segments,i.e., text, audio, image or video segments that correspond to the eventsthat occur in the media, e.g., video and for example as detected throughanalysis of the motion capture data.

Some systems that are related to monitoring impacts are focused onlinear acceleration related impacts. These systems are unable to monitorrotational accelerations or velocities and are therefore unable todetect certain types of events that may produce concussions. Inaddition, many of these types of systems do not produce event related,connectionless messages for low power and longevity considerations.Hence, these systems are limited in their use based on their lack ofrobust characteristics.

Known systems also do not contemplate data mining of events withinmotion data to form a representation of a particular movement, forexample a swing of an average player or average professional playerlevel, or any player level based on a function of events recognizedwithin previously stored motion data. Thus, it is difficult and timeconsuming and requires manual labor to find, trim and designateparticular motion related events for use in virtual reality for example.Hence, current systems do not easily enable a particular user to playagainst a previously stored motion event of the same user or other useralong with a historical player for example. Furthermore, known systemsdo not take into account cumulative impacts, and for example withrespect to data mined information related to concussions, to determineif a series of impacts may lead to impaired brain function over time. Noknown systems integrate media and sensor data and determine, confirm andpublish events, or curated events based on the combination of media andsensor data.

Other types of motion capture systems include video systems that aredirected at analyzing and teaching body mechanics. These systems arebased on video recording of an athlete and analysis of the recordedvideo of an athlete. This technique has various limitations includinginaccurate and inconsistent subjective analysis based on video forexample. Another technique includes motion analysis, for example usingat least two cameras to capture three-dimensional points of movementassociated with an athlete. Known implementations utilize a stationarymulti-camera system that is not portable and thus cannot be utilizedoutside of the environment where the system is installed, for exampleduring an athletic event such as a golf tournament, football game or tomonitor a child or elderly person. In general video based systems do notalso utilize digital motion capture data from sensors on the objectundergoing motion since they are directed at obtaining and analyzingimages having visual markers instead of electronic sensors. These fixedinstallations are extremely expensive as well. Such prior techniques aresummarized in U.S. Pat. No. 7,264,554, filed 26 Jan. 2006, which claimsthe benefit of U.S. Provisional Patent Application Ser. No. 60/647,751filed 26 Jan. 2005, the specifications of which are both herebyincorporated herein by reference. Both disclosures are to the sameinventor of the subject matter of the instant application.

Regardless of the motion capture data obtained, the data is generallyanalyzed on a per user or per swing basis that does not contemplateprocessing on a mobile phone, so that a user would only buy a motioncapture sensor and an “app” for a pre-existing mobile phone. Inaddition, existing solutions do not contemplate mobile use, analysis andmessaging and/or comparison to or use of previously stored motioncapture data from the user or other users or data mining of large datasets of motion capture data, for example to obtain or create motioncapture data associated with a group of users, for example professionalgolfers, tennis players, baseball players or players of any other sportto provide events associated with a “professional level” average orexceptional virtual reality opponent. To summarize, motion capture datais generally used for immediate monitoring or sports performancefeedback and generally has had limited and/or primitive use in otherfields.

Known motion capture systems generally utilize several passive or activemarkers or several sensors. There are no known systems that utilize aslittle as one visual marker or sensor and an app that for exampleexecutes on a mobile device that a user already owns, to analyze anddisplay motion capture data associated with a user and/or piece ofequipment. The data is generally analyzed in a laboratory on a per useror per swing basis and is not used for any other purpose besides motionanalysis or representation of motion of that particular user and isgenerally not subjected to data mining.

There are no known systems that allow for motion capture elements suchas wireless sensors to seamlessly integrate or otherwise couple with auser or shoes, gloves, shirts, pants, belts, or other equipment, such asa baseball bat, tennis racquet, golf club, mouth piece for a boxer,football or soccer player, or protective mouthpiece utilized in anyother contact sport for local analysis or later analysis in such a smallformat that the user is not aware that the sensors are located in or onthese items. There are no known systems that provide seamless mounts,for example in the weight port of a golf club or at the end shaft nearthe handle so as to provide a wireless golf club, configured to capturemotion data. Data derived from existing sensors is not saved in adatabase for a large number of events and is not used relative toanything but the performance at which the motion capture data wasacquired.

In addition, for sports that utilize a piece of equipment and a ball,there are no known portable systems that allow the user to obtainimmediate visual feedback regarding ball flight distance, swing speed,swing efficiency of the piece of equipment or how centered an impact ofthe ball is, i.e., where on the piece of equipment the collision of theball has taken place. These systems do not allow for user's to playgames with the motion capture data acquired from other users, orhistorical players, or from their own previous performances. Knownsystems do not allow for data mining motion capture data from a largenumber of swings to suggest or allow the searching for better or optimalequipment to match a user's motion capture data and do not enableoriginal equipment manufacturers (OEMs) to make business decisions,e.g., improve their products, compare their products to othermanufacturers, up-sell products or contact users that may purchasedifferent or more profitable products.

In addition, there are no known systems that utilize motion capture datamining for equipment fitting and subsequent point-of-sale decisionmaking for instantaneous purchasing of equipment that fits an athlete.Furthermore, no known systems allow for custom order fulfillment such asassemble-to-order (ATO) for custom order fulfillment of sportingequipment, for example equipment that is built to customerspecifications based on motion capture data mining, and shipped to thecustomer to complete the point of sales process, for example during playor virtual reality play. Known systems do not publish any of thisinformation on social media sites.

In addition, there are no known systems that use a mobile device andRFID tags for passive compliance and monitoring applications.

There are no known systems that enable data mining for a large number ofusers related to their motion or motion of associated equipment to findpatterns in the data that allows for business strategies to bedetermined based on heretofore undiscovered patterns related to motion.There are no known systems that enable obtain payment from OEMs, medicalprofessionals, gaming companies or other end users to allow data miningof motion data.

Known systems such as Lokshin, United States Patent Publication No.20130346013, published 26 Dec. 2013 and 2013033054 published 12 Dec.2013 for example do not contemplate uploading only the pertinent videosthat occur during event, but rather upload large videos that are latersynchronized. Both Lokshin references does not contemplate a motioncapture sensor commanding a camera to alter camera parameters on-the-flybased on the event, to provide increased frame rate for slow motion forexample during the event video capture, and do not contemplate changingplayback parameters during a portion of a video corresponding to anevent. The references also do not contemplate generation of highlightreels where multiple cameras may capture an event, for example from adifferent angle and do not contemplate automatic selection of the bestvideo for a given event. In addition, the references do not contemplatea multi-sensor environment where other sensors may not observe orotherwise detect an event, while the sensor data is still valuable forobtaining metrics, and hence the references do not teach saving eventdata on other sensors after one sensor has identified an event.

Associating one or more tags with events is often useful for eventanalysis, filtering, and categorizing. Tags may for example indicate theplayers involved in an event, the type of action, and the result of anaction (such as a score). Known systems rely on manual tagging of eventsby human operators who review event videos and event data. For example,there are existing systems for coaches to tag videos of sporting eventsor practices, for example to review a team's performance or for scoutingreports. There are also systems for sports broadcasting that manuallytag video events with players or actions. There are no known systemsthat analyze data from motion sensors, and media, e.g., video, radar, orother sensors to automatically select one or more tags for an eventbased on the data. An automatic event tagging system would provide asignificant labor saving over the current manual tagging methods, andwould provide valuable information for subsequent event retrieval andanalysis. Known systems are unable to detect, confirm and publish eventsbased on sensor data and media since they do not integrate theinformation obtained from these disparate sources.

For at least the limitations described above there is a need for anevent detection, confirmation and publication system that integratessensor data and social media.

BRIEF SUMMARY OF THE INVENTION

Embodiments of the invention enable an event detection, confirmation andpublication system that integrates sensor data and social media.Embodiments utilize information from sensors in combination with mediato detect and confirm events that occur generally in a particular timerange and area, or particular range about a location. Sensors mayinclude for example inertial sensors or motion capture sensors thatobtain one or more values associated with orientation, position,velocity, acceleration, angular velocity, angular acceleration, as wellas other sensors such as physical sensors, environmental sensors,chemical sensors and physiological sensors, i.e., sensors that obtainone or more values associated with electromagnetic field, temperature,humidity, wind, pressure, elevation, light, sound, heart rate, etc. Byintelligently analyzing the sensor data and media, such as social mediafor a given time duration and area near a location, the event can bedetermined and confirmed and then if desired, published, for example tosocial media. Embodiments enable motion capture data and other sensordata to be utilized to curate text, sound, images, or 360 images, andvideo, or 360 video, and post the results to social networks, forexample in a dedicated feed, on a single user's timeline or on multipleuser's timelines. Embodiments of the system may also differentiatesimilar types of motion events to determine the type of equipment oractivity or quality of the event, such as how proficient a user is at acertain activity. Embodiments of the system also may post or filter tosocial media sites for example using any other filter besides locationand time and the media, or text, audio, image or video in the socialmedia posts for example. Embodiments may also use inertial or motion orother sensor data to define and event, eliminate false positive events,post true events, and/or correlate the events with social media toconfirm the events, or post the events, or determine if an event is fakenews. Embodiments of the system may utilize any algorithm based on theintegrated sensor data and media to determine whether an event is validor invalid or represents fake news including text analysis, audioanalysis, image analysis, video analysis or artificial intelligence,natural language processing, affect analysis or any other method. One ormore embodiments may be utilized to analyze multiple social media posts,or threads that are unknown across “friends” to determine events and/orprovide emergency notifications to for example flash all mobile devicescreens in case of a local emergency or terrorist attack.

Embodiments of the invention also enable intelligent synchronization andtransfer of generally concise event videos synchronized with motion datafrom motion capture sensor(s) coupled with a user or piece of equipment.At least one embodiments of the invention greatly saves storage andincreases upload speed by uploading event media, for example eventvideos and avoiding upload of non-pertinent portions of large videos.Provides intelligent selection of multiple videos from multiple camerascovering an event at a given time, for example selecting one with leastshake. Video and other media describing an event may be obtained from aserver, such as a social media site. Enables near real-time alterationof camera parameters during an event determined by the motion capturesensor, and alteration of playback parameters and special effects forsynchronized event videos. Creates highlight reels filtered by metricsand can sort by metric. Integrates with multiple sensors to save eventdata even if other sensors do not detect the event. Also enablesanalysis or comparison of movement associated with the same user, otheruser, historical user or group of users. At least one embodimentprovides intelligent recognition of events within motion data includingbut not limited to motion capture data obtained from portable wirelessmotion capture elements such as visual markers and sensors, radiofrequency identification tags and mobile device computer systems, orcalculated based on analyzed movement associated with the same user, orcompared against the user or another other user, historical user orgroup of users. Enables low memory utilization for event data and videodata by trimming motion data and videos to correspond to the detectedevents. This may be performed on the mobile device, which may includesmart glasses having camera(s) and/or at least one processor, or on aremote server and based on location and/or time of the event and basedon the location and/or time of the video, and may optionally include theorientation of the camera to further limit the media, for example text,audio, images or videos that may include the events or motion events.Embodiments enable event based publication and/or viewing and low powertransmission of events and communication with an app executing on amobile device and/or with external cameras to designate windows thatdefine the events. Enables recognition of motion events, and designationof events within images or videos, such as a shot, move or swing of aplayer, a concussion of a player, boxer, rider or driver, or a heatstroke, hypothermia, seizure, asthma attack, epileptic attack or anyother sporting or physical motion related event including walking andfalling. Events may be correlated with one or more images or video ascaptured from internal/external camera or cameras or nanny cam, forexample to enable saving video of the event, such as the first steps ofa child, violent shaking events, sporting events including concussions,or falling events associated with an elderly person. Concussion relatedevents and other events may be monitored for linear accelerationthresholds and/or patterns as well as rotational acceleration andvelocity thresholds and/or patterns and/or saved on an event basisand/or transferred over lightweight connectionless protocols or anycombination thereof.

Embodiments of the invention enable a user to purchase an application or“app” and a motion capture element and immediately utilize the systemwith their existing mobile computer, e.g., mobile phone. Embodiments ofthe invention may display motion information to a monitoring user, oruser associated with the motion capture element or piece of equipment.Embodiments may also display information based on motion analysis dataassociated with a user or piece of equipment based on (via a functionsuch as but not limited to a comparison) previously stored motioncapture data or motion analysis data associated with the user or pieceof equipment or previously stored motion capture data or motion analysisdata associated with at least one other user. This enables sophisticatedmonitoring, compliance, interaction with actual motion capture data orpattern obtained from other user(s), for example to play a virtual gameusing real motion data obtained from the user with responses generatedbased thereon using real motion data capture from the user previously orfrom other users (or equipment). This capability provides for playingagainst historical players, for example a game of virtual tennis, orplaying against an “average” professional sports person, and is unknownin the art until now.

For example, one or more embodiments include at least one motion captureelement that may couple with a user or piece of equipment or mobiledevice coupled with the user, wherein the at least one motion captureelement includes a memory, such as a sensor data memory, and a sensorthat may capture any combination of values associated with anorientation, position, velocity, acceleration (linear and/orrotational), angular velocity and angular acceleration, of the at leastone motion capture element. In at least one embodiment, the at least onemotion capture element may include a first communication interface or atleast one other sensor, and a microcontroller coupled with the memory,the sensor and the first communication interface.

According to at least embodiment of the invention, the microcontrollermay be a microprocessor. By way of one or more embodiments, the firstcommunication interface may receive one or more other values associatedwith a temperature, humidity, wind, elevation, light sound, heart rate,or any combination thereof. In at least one embodiment, the at least oneother sensor may locally capture the one or more other values associatedwith the temperature, humidity, wind, elevation, light sound, heartrate, or any combination thereof. At least one embodiment of theinvention may include both the first communication interface and the atleast one other sensor to obtain motion data and/or environmental orphysiological data in any combination. In other embodiments, theprocessor in a mobile device such as smart glasses, a cell phone, tabletor laptop may interface directly with sensors or communicate over acommunications interface to obtain the sensor values that may not becoupled to a microcontroller or microprocessor.

The microcontroller or microprocessor is configured to collect data thatincludes sensor values from the sensor, store the data in the memory,analyze the data and recognize an event within the data to determineevent data. In at least one embodiment, the microprocessor may correlatethe data or the event data with the one or more other values associatedwith the temperature, humidity, wind, elevation, light sound, heartrate, etc., or any combination thereof. As such, in at least oneembodiment, the microprocessor may correlate the data or the event datawith the one or more other values to determine one or more of a falsepositive event, a type of equipment that the at least one motion captureelement is coupled with, and a type of activity indicated by the data orthe event data.

In one or more embodiments, the microprocessor may transmit one or moreof the data and the event data associated with the event via the firstcommunication interface. Embodiments of the system may also include anapplication that executes on a mobile device, wherein the mobile deviceincludes a computer, a communication interface that communicates withthe communication interface of the motion capture element to obtain theevent data associated with the event. In at least one embodiment, thecomputer may couple with a communication interface, such as the firstcommunication interface, wherein the computer executes the applicationor “app” to configure the computer to receive one or more of the dataand the event data from the communication interface, analyze the dataand event data to form motion analysis data, store the data and eventdata, or the motion analysis data, or both the event data and the motionanalysis data, and display information including the event data, or themotion analysis data, or both associated with the at least one user on adisplay.

In one or more embodiments, the microprocessor may detect the type ofequipment the at least one motion capture sensor is coupled with or thetype of activity the at least one motion sensor is sensing through thecorrelation to differentiate a similar motion for a first type ofactivity with respect to a second type of activity. In at least oneembodiment, the at least one motion capture sensor may differentiate thesimilar motion based on the one or more values associated withtemperature, humidity, wind, elevation, light, sound, heart rate, etc.,or any combination thereof.

By way of one or more embodiments, the microprocessor may detect thetype of equipment or the type of activity through the correlation todifferentiate a similar motion for a first type of activity includingsurfing with respect to a second type of activity includingsnowboarding. In at least one embodiment, the microprocessor maydifferentiate the similar motion based on the temperature or thealtitude or both the temperature and the altitude. In at least oneembodiment, the microprocessor may recognize a location of the sensor onthe piece of equipment or the user based on the data or event data. Inone or more embodiments, the microprocessor may collect data thatincludes sensor values from the sensor based on a sensor personalityselected from a plurality of sensor personalities. In at least oneembodiment, the sensor personality may control sensor settings tocollect the data in an optimal manner with respect to a specific type ofmovement or the type of activity associated with a specific piece ofequipment or type of clothing.

By way of one or more embodiments, the microprocessor may determine thefalse positive event as detect a first value from the sensor valueshaving a first threshold value and detect a second value from the sensorvalues having a second threshold value within a time window. In at leastone embodiment, the microprocessor may then signify a prospective event,compare the prospective event to a characteristic signal associated witha typical event and eliminate any false positive events, signify a validevent if the prospective event is not a false positive event, and savethe valid event in the sensor data memory including information withinan event time window as the data.

In at least one embodiment, the at least one motion capture element maybe contained within a motion capture element mount, a mobile device, amobile phone, a smart phone, glasses equipped with at least one camera,a smart watch, a camera, a laptop computer, a notebook computer, atablet computer, a desktop computer, a server computer or anycombination thereof.

In one or more embodiments, the microprocessor may recognize the atleast one motion capture element with newly assigned locations after theat least one motion capture element is removed from the piece ofequipment and coupled with a second piece of equipment of a differenttype based on the data or event data.

In at least one embodiment, the system may include a computer whereinthe computer may include a computer memory, a second communicationinterface that may communicate with the first communication interface toobtain the data or the event data associated with the event or both thedata the event data. In one or more embodiments, the computer may becoupled with the computer memory and the second communication interface,wherein the computer may receive the data from the second communicationinterface and analyze the data and recognize an event within the data todetermine event data. In at least one embodiment, the computer mayreceive the event data from the second communication interface, or mayreceive both the data and the event data from the second communicationinterface.

In one or more embodiments, the computer may analyze the event data toform motion analysis data, store the event data, or the motion analysisdata, or both the event data and the motion analysis data in thecomputer memory, obtain an event start time and an event stop time fromthe event data, and obtain at least one video start time and at leastone video stop time associated with at least one video. In at least oneembodiment, the computer may synchronize the event data, the motionanalysis data or any combination thereof with the at least one type ofmedia, i.e., text, audio, image or video. In one or more embodiments,the computer may synchronize based on the first time associated with thedata or the event data obtained from the at least one motion captureelement coupled with the user or the piece of equipment or the mobiledevice coupled with the user, and at least one time associated with theat least one video to create at least one synchronized event, e.g.,having text, audio, image or video or any combination thereof. In atleast one embodiment, the computer may store the at least onesynchronized event, for example event video in the computer memorywithout at least a portion of the at least one video outside of theevent start time to the event stop time.

By way of one or more embodiments, the computer may include at least oneprocessor in a mobile device, a mobile phone, a smart phone, glasseshaving at least one camera, a smart watch, a camera, a laptop computer,a notebook computer, a tablet computer, a desktop computer, a servercomputer or any combination of any number of the mobile device, mobilephone, smart phone, glasses having at least one camera, smart watch,camera, laptop computer, notebook computer, tablet computer, desktopcomputer and server computer.

According to at least one embodiment, the computer may display asynchronized event media, e.g., event video including both of the eventdata, motion analysis data or any combination thereof that occurs duringa timespan from the event start time to the event stop time, and thevideo captured during the timespan from the event start time to theevent stop time.

In one or more embodiments, the computer may transmit the at least onesynchronized event video or a portion of the at least one synchronizedevent video to one or more of a repository, a viewer, a server, anothercomputer, a social media site, a mobile device, a network, and anemergency service.

In at least one embodiment, the computer may accept a metric associatedwith the at least one synchronized event video, and accept selectioncriteria for the metric. In one or more embodiments, the computer maydetermine a matching set of synchronized event videos that have valuesassociated with the metric that pass the selection criteria, and displaythe matching set of synchronized event videos or correspondingthumbnails thereof along with the value associated with the metric foreach of the matching set of synchronized event videos or thecorresponding thumbnails. Other types of media including text, audio andimage media may also be selected based on a metric.

In at least one embodiment of the invention, the sensor or the computermay include a microphone that records audio signals. In one or moreembodiments, the recognize an event may include determining aprospective event based on the data, and correlating the data with theaudio signals to determine if the prospective event is a valid event ora false positive event. In at least one embodiment, the computer maystore the audio signals in the computer memory with the at least onesynchronized event video if the prospective event is a valid event.

One or more embodiments include at least one motion capture sensor thatmay be placed near the user's head wherein the microcontroller ormicroprocessor may calculate a location of impact on the user's head.Embodiments of the at least one motion capture sensor may be coupled ona hat or cap, within a protective mouthpiece, using any type of mount,enclosure or coupling mechanism. One or more embodiments of the at leastone motion capture sensor may be coupled with a helmet on the user'shead and wherein the calculation of the location of impact on the user'shead is based on the physical geometry of the user's head and/or helmet.Embodiments may include a temperature sensor coupled with the at leastone motion capture sensor or with the microcontroller, ormicroprocessor, for example.

Embodiments of the invention may also utilize an isolator to surroundthe at least one motion capture element to approximate physicalacceleration dampening of cerebrospinal fluid around the user's brain tominimize translation of linear acceleration and rotational accelerationof the event data to obtain an observed linear acceleration and anobserved rotational acceleration of the user's brain. Thus, embodimentsmay eliminate processing to translate forces or acceleration values orany other values from the helmet based acceleration to the observedbrain acceleration values. Therefore, embodiments utilize less power andstorage to provide event specific data, which in turn minimizes theamount of data transfer, which yields lower transmission powerutilization and even lower total power utilization. Different isolatorsmay be utilized on a football/hockey/lacrosse player's helmet based onthe type of padding inherent in the helmet. Other embodiments utilizedin sports where helmets are not worn, or occasionally worn may alsoutilize at least one motion capture sensor on a cap or hat, for exampleon a baseball player's hat, along with at least one sensor mounted on abatting helmet. Headband mounts may also be utilized in sports where acap is not utilized, such as soccer to also determine concussions. Inone or more embodiments, the isolator utilized on a helmet may remain inthe enclosure attached to the helmet and the sensor may be removed andplaced on another piece of equipment that does not make use of anisolator that matches the dampening of a user's brain fluids.Embodiments may automatically detect a type of motion and determine thetype of equipment that the motion capture sensor is currently attachedto based on characteristic motion patterns associated with certain typesof equipment, i.e., surfboard versus baseball bat, snow board and skateboard, etc.

Embodiments of the invention may obtain/calculate a linear accelerationvalue or a rotational acceleration value or both. This enablesrotational events to be monitored for concussions as well as linearaccelerations. In one or more embodiments, other events may make use ofthe linear and/or rotational acceleration and/or velocity, for exampleas compared against patterns or templates to not only switch sensorpersonalities during an event to alter the capture characteristicsdynamically, but also to characterize the type of equipment currentlybeing utilized with the current motion capture sensor. As such, in atleast one embodiment, a single motion capture element may be purchasedby a user to instrument multiple pieces of equipment or clothing byenabling the sensor to automatically determine what type of equipment orpiece of clothing the sensor is coupled to based on the motion capturedby the sensor when compared against characteristic patterns or templatesof motion.

Embodiments of the invention may transmit the event data associated withthe event using a connectionless broadcast message. In one or moreembodiments, depending on the communication protocol employed, broadcastmessages may include payloads with a limited amount of data that may beutilized to avoid handshaking and overhead of a connection basedprotocol. In other embodiments, connectionless or connection basedprotocols may be utilized in any combination.

In one or more embodiments, the computer may access previously storedevent data or motion analysis data associated with at least one otheruser, or the user, or at least one other piece of equipment, or thepiece of equipment, for example to determine the number of concussionsor falls or other swings, or any other motion event. Embodiments mayalso display information including a presentation of the event dataassociated with the at least one user on a display based on the eventdata or motion analysis data associated with the user or piece ofequipment and the previously stored event data or motion analysis dataassociated with the user or piece of equipment or with the at least oneother user or the at least one other piece of equipment. This enablescomparison of motion events, in number or quantitative value, e.g., themaximum rotational acceleration observed by the user or other users in aparticular game or historically. In addition, in at least oneembodiment, patterns or templates that define characteristic motion ofparticular pieces of equipment for typical events may be dynamicallyupdated, for example on a central server or locally, and dynamicallyupdated in motion capture sensors via the communication interface in oneor more embodiments. This enables sensors to improve over time.

Embodiments of the invention may transmit the information to a displayon a visual display coupled with the computer or a remote computer, forexample over broadcast television or the Internet for example.Embodiments of the display may also accept sub-event time locations toprovide discrete scrolling along the timeline of the whole event. Forexample, a golf swing may include sub-events such as an address, swingback, swing forward, strike, follow through. The system may display timelocations for the sub-events and accept user input near the location toassert that the video should start or stop at that point in time, orscroll to or back to that point in time for ease of viewing sub-eventsfor example.

Embodiments of the invention may also include an identifier coupled withthe at least one motion capture sensor or the user or the piece ofequipment. In one or more embodiments, the identifier may include a teamand jersey number or student identifier number or license number or anyother identifier that enables relatively unique identification of aparticular event from a particular user or piece of equipment. Thisenables team sports or locations with multiple players or users to beidentified with respect to the app that may receive data associated witha particular player or user. One or more embodiments receive theidentifier, for example a passive RFID identifier or MAC address orother serial number associated with the player or user and associate theidentifier with the event data and motion analysis data.

One or more embodiments of the at least one motion capture element mayfurther include a light emitting element that may output light if theevent occurs. This may be utilized to display a potential, mild orsevere level of concussion on the outer portion of the helmet withoutany required communication to any external device for example. Differentcolors or flashing intervals may also be utilized to relay informationrelated to the event. Alternatively, or in combination, the at least onemotion capture element may further include an audio output element thatmay output sound if the event occurs or if the at least one motioncapture sensor is out of range of the computer or wherein the computermay display and alert if the at least one motion capture sensor is outof range of the computer, or any combination thereof. Embodiments of thesensor may also utilize an LCD that outputs a coded analysis of thecurrent event, for example in a Quick Response (QR) code or bar code forexample so that a referee may obtain a snapshot of the analysis code ona mobile device locally, and so that the event is not viewed in areadable form on the sensor or transmitted and intercepted by anyoneelse.

In one or more embodiments, the at least one motion capture elementfurther includes a location determination element coupled with themicrocontroller. This may include a GPS (Global Positioning System)device for example. Alternatively, or in combination, the computer maytriangulate the location in concert with another computer, or obtain thelocation from any other triangulation type of receiver, or calculate thelocation based on images captured via a camera coupled with the computerand known to be oriented in a particular direction, wherein the computercalculates an offset from the mobile device based on the direction andsize of objects within the image for example.

In one or more embodiments, the computer may to request at least oneimage or video that contains the event from at least one camera proximalto the event. This may include a broadcast message requesting video froma particular proximal camera or a camera that is pointing in thedirection of the event. In one or more embodiments, the computer maybroadcast a request for camera locations proximal to the event ororiented to view the event, and optionally display the availablecameras, or videos therefrom for the time duration around the event ofinterest. In one or more embodiments, the computer may display a list ofone or more times at which the event has occurred, which enables theuser obtain the desired event video via the computer, and/or toindependently request the video from a third party with the desiredevent times. For example, one or more embodiments may obtain a video orother media, such as images, text, or audio, from a social media server.

In one or more embodiments, the at least one motion capture sensor iscoupled with the mobile device and for example uses an internal motionsensor within or coupled with the mobile device. This enables motioncapture and event recognition with minimal and ubiquitous hardware,e.g., using a mobile device with a built-in accelerometer. In one ormore embodiments, a first mobile device may be coupled with a userrecording motion data, while a second mobile device is utilized torecord a video of the motion. In one or more embodiments, the userundergoing motion may gesture, e.g., tap N times on the mobile device toindicate that the second user's mobile device should start recordingvideo or stop recording video. Any other gesture may be utilized tocommunicate event related or motion related indications between mobiledevices.

Embodiments of the at least one motion capture sensor may include atemperature sensor, or the microcontroller may otherwise be coupled witha temperature sensor. In these embodiments, the microcontroller ormicroprocessor may transmit a temperature obtained from the temperaturesensor as a temperature event, for example as a potential indication ofheat stroke or hypothermia. Any other type of physiological sensor maybe utilized, as well as any type of environmental sensor.

Thus embodiments of the invention may recognize any type of motionevent, including events related to motion associated with the at leastone motion capture sensor coupled with any combination of the user, orthe piece of equipment or the mobile device or motion that is indicativeof standing, walking, falling, a heat stroke, seizure, violent shaking,a concussion, a collision, abnormal gait, abnormal or non-existentbreathing or any combination thereof or any other type of event having aduration of time during with motion occurs. For example, one or moreembodiments may include an accelerometer in a motion capture element,and may recognize an event when the acceleration reading from theaccelerometer exceeds a predefined threshold. Such events may correspondto the motion capture element experiencing significant forces, which insome embodiments may indicate events of interest. One or moreembodiments may in addition or instead use for example the change inacceleration as an indicator of an event, since a rapid change inacceleration may indicate a shock or impact event. Embodiments may useany sensors and any functions of sensor data to detect events.

Embodiments of the invention may utilize data mining on the motioncapture data to obtain patterns for users, equipment, or use the motioncapture data or events of a given user or other user in particularembodiments of the invention. Data mining relates to discovering newpatterns in large databases wherein the patterns are previously unknown.Many methods may be applied to the data to discover new patternsincluding statistical analysis, neural networks and artificialintelligence for example. Due to the large amount of data, automateddata mining may be performed by one or more computers to find unknownpatterns in the data. Unknown patterns may include groups of relateddata, anomalies in the data, dependencies between elements of the data,classifications and functions that model the data with minimal error orany other type of unknown pattern. Displays of data mining results mayinclude displays that summarize newly discovered patterns in a way thatis easier for a user to understand than large amounts of pure raw data.One of the results of the data mining process is improved marketresearch reports, product improvement, lead generation and targetedsales. Generally, any type of data that will be subjected to data miningmust be cleansed, data mined and the results of which are generallyvalidated. Businesses may increase profits using data mining. Examplesof benefits of embodiments of the invention include customerrelationship management to highly target individuals based on patternsdiscovered in the data. In addition, market basket analysis data miningenables identifying products that are purchased or owned by the sameindividuals and which can be utilized to offer products to users thatown one product but who do not own another product that is typicallyowned by other users.

Other areas of data mining include analyzing large sets of motion datafrom different users to suggest exercises to improve performance basedon performance data from other users. For example, if one user has lessrotation of the hips during a swing versus the average user, thenexercises to improve flexibility or strength may be suggested by thesystem. In a golf course embodiment, golf course planners may determineover a large amount of users on a golf course which holes should beadjusted in length or difficulty to obtain more discrete values for theaverage number of shots per hole, or for determining the amount of timebetween golfers, for example at a certain time of day or for golfers ofa certain age. In addition, sports and medical applications of datamining include determining morphological changes in user performanceover time, for example versus diet or exercise changes to determine whatimproves performance the most, or for example what times of the day,temperatures, or other conditions produce swing events that result inthe furthest drive or lowest score. Use of motion capture data for aparticular user or with respect to other users enables healthcarecompliance, for example to ensure a person with diabetes moves a certainamount during the day, and morphological analysis to determine how auser's motion or range of motion has changed over time. Games may beplayed with motion capture data that enables virtual reality playagainst historical greats or other users. For example, a person may playagainst a previous performance of the same person or against the motioncapture data of a friend. This allows users to play a game in a historicstadium or venue in a virtual reality environment, but with motioncapture data acquired from the user or other users previously forexample. Military planners may utilize the motion capture data todetermine which soldiers are most fit and therefore eligible for specialoperations, or which ones should retire, or by coaches to determine whena player should rest based on the concussion events and severity thereofsustained by a player for example and potentially based on a mined timeperiod where other users have increased performance after a concussionrelated event.

Embodiments of the system perform motion capture and/or display with anapplication for example that executes on mobile device that may includea visual display and an optional camera and which is capable ofobtaining data from at least one motion capture element such as a visualmarker and/or a wireless sensor. The system can also integrate withstandalone cameras, or cameras on multiple mobile devices. The systemalso enables the user to analyze and display the motion capture data ina variety of ways that provide immediate easy to understand graphicalinformation associated with the motion capture data. Motion captureelements utilized in the system intelligently store data for examplerelated to events associated with striking a ball, making a ski turn,jumping, etc., and eliminate false events, and greatly improve memoryusage and minimize storage requirements. In addition, the data may bestored for example for more than one event associated with the sportingequipment, for example multiple bat swings or for an entire round ofgolf or more if necessary at least until the data is downloaded to amobile device or to the Internet. Data compression of captured data mayalso be utilized to store more motion capture data in a given amount ofmemory. Motion capture elements utilized in the system may intelligentlypower down portions of their circuitry to save power, for example powerdown transceivers until motion is detected of a certain type.Embodiments of the invention may also utilize flexible batteryconnectors to couple two or more batteries in parallel to increase thetime the system may be utilized before replacing the batteries. Motioncapture data is generally stored in memory such as a local database orin a network accessible database, any of which enables data miningdescribed above. Any other type of data mining may be performed usingembodiments of the invention, including searching for temporal changesof data related to one or more users and or simply searching for datarelated to a particular user or piece of equipment.

Other embodiments may display information such as music selections ormusic playlists to be played based on the motion related data. This forexample enables a performance to be compared to another user'sperformance and select the type of music the other user plays, or tocompare the performance relative to a threshold that determines whattype of music selection to suggest or display.

Embodiments of the invention directed sports for example enable RFID orpassive RFID tags to be placed on items that a user moves whereinembodiments of the system keep track of the motion. For example, byplacing passive RFID tags on a particular helmet or cap, or protectivemouthpiece for boxing, football, soccer or other contact sport,particular dumbbells at a gym, and by wearing motion capture elementssuch as gloves and with a pre-existing mobile device for example anIPHONE®, embodiments of the invention provide automatic safetycompliance or fitness and/or healthcare compliance. This is achieved bykeeping track of the motion, and via RFID or passive RFID, the weightthat the user is lifting. Embodiments of the invention may thus add thenumber of repetitions multiplied by the amount of weight indicated byeach RFID tag to calculate the number of calories burned by the user. Inanother example, an RFID tag coupled with a stationary bike, or whereinthe stationary bike can mimic the identifier and/or communicatewirelessly to provide performance data and wherein the mobile computerincludes an RFID reader, the number of rotations of the user's legs maybe counted. Any other use of RFID or passive RFID is in keeping with thespirit of the invention. This enables doctors to remotely determinewhether a user has complied with their medical recommendations, orexceeded linear or rotational acceleration indicative of a concussionfor example. Embodiments may thus be utilized by users to ensurecompliance and by doctors to lower their malpractice insurance ratessince they are ensuring that their patients are complying with theirrecommendations, albeit remotely. Embodiments of the invention do notrequire RFID tags for medical compliance, but may utilize them.Embodiments of the invention directed at golf also enable golf shots foreach club associated with a golfer to be counted through use of anidentifier such as RFID tags on each club (or optionally via anidentifier associated with motion capture electronics on a golf club orobtained remotely over the radio) and a mobile computer, for example anIPHONE® equipped with an RFID reader that concentrates the processingfor golf shot counting on the mobile computer instead of on each golfclub. Embodiments of the invention may also allow for the measurement oforientation (North/South, and/or two horizontal axes and the verticalaxis) and acceleration using an inertial measurement unit, oraccelerometers and/or magnetometers, and/or gyroscopes. This is notrequired for golf shot counting, although one or more embodiments maydetermine when the golf club has struck a golf ball through vibrationanalysis for example and then query a golfer whether to count a shot ornot. This functionality may be combined with speed or accelerationthreshold or range detection for example to determine whether the golfclub was travelling within an acceptable speed or range, or accelerationor range for the “hit” to count. Wavelets may also be utilized tocompare valid swing signatures to eliminate count shots or eliminatefalse strikes for example. This range may vary between different clubs,for example a driver speed range may be “greater than 30 mph” while aputter speed range may be “less than 20 mph”, any range may be utilizedwith any club as desired, or the speed range may be ignored for example.Alternatively, or in combination, the mobile computer may only query thegolfer to count a shot if the golfer is not moving laterally, i.e., in agolf cart or walking, and/or wherein the golfer may have rotated ortaken a shot as determined by an orientation or gyroscope sensor coupledwith the mobile computer. The position of the stroke may be shown on amap on the mobile computer for example. In addition, GPS receivers withwireless radios may be placed within the tee markers and in the cups togive daily updates of distances and helps with reading putts and greensfor example. The golfer may also wear virtual glasses that allow thegolfer to see the golf course map, current location, distance to thehole, number of shots on the current hole, total number of shots and anyother desired metric. If the user moves a certain distance, asdetermined by GPS for example, from the shot without counting the shot,the system may prompt the user on whether to count the shot or not. Thesystem does not require a user to initiate a switch on a club to count ashot and does not require LED's or active or battery powered electronicson each club to count shots. The mobile computer may also acceptgestures from the user to count a shot or not count a shot so that thegolfer does not have to remove any gloves to operate the mobilecomputer. For embodiments that utilize position/orientation sensors, thesystem may only count shots when a club is oriented vertically forexample when an impact is detected. The apparatus may also includeidentifiers that enable a specific apparatus to be identified. Theidentifiers may be a serial number for example. The identifier forexample may originate from an RFID tag on each golf club, or optionallymay include a serial number or other identifier associated with motioncapture elements associated with a golf club. Utilizing this apparatusenables the identification of a specific golfer, specific club and alsoenables motion capture and/or display with a system that includes atelevision and/or mobile device having a visual display and an optionalcamera and capable of obtaining data from at least one motion captureelement such as a visual marker and/or a wireless sensor. The system canalso integrate with standalone cameras, or cameras on multiple mobiledevices. The system also enables the user to analyze and display themotion capture data in a variety of ways that provide immediate and easyto understand graphical information associated with the motion capturedata. The apparatus enables the system to also determine how “centered”an impact is with respect to a ball and a piece of equipment, such as agolf club for example. The system also allows for fitting of equipmentincluding shoes, clubs, etc., and immediate purchasing of the equipmenteven if the equipment requires a custom assemble-to-order request from avendor. Once the motion capture data, videos or images and shot countindications are obtained by the system, they may be stored locally, forexample in a local database or sent over a wired or wireless interfaceto a remote database for example. Once in a database, the variouselements including any data associated with the user, such as age, sex,height, weight, address, income or any other related information may beutilized in embodiments of the invention and/or subjected to datamining. One or more embodiments enable users or OEMs for example to payfor access to the data mining capabilities of the system.

For example, embodiments that utilize motion capture elements allow foranalyzing the data obtained from the apparatus and enable thepresentation of unique displays associated with the user, such as 3Doverlays onto images of the body of the user to visually depict thecaptured motion data. In addition, these embodiments may also utilizeactive wireless technology such as BLUETOOTH® Low Energy for a range ofup to 50 meters to communicate with a golfer's mobile computer.Embodiments of the invention also allow for display of queries forcounting a stroke for example as a result of receiving a golf club ID,for example via an RFID reader or alternatively via wirelesscommunication using BLUETOOTH® or IEEE 802.11 for example. Use ofBLUETOOTH® Low Energy chips allows for a club to be in sleep mode for upto 3 years with a standard coin cell battery, thus reducing requiredmaintenance. One or more embodiments of the invention may utilize morethan one radio, of more than one technology for example. This allows fora level of redundancy that increases robustness of the system. Forexample, if one radio no longer functions, e.g., the BLUETOOTH® radiofor example, then the IEEE 802.11 radio may be utilized to transfer dataand warn the golfer that one of the radios is not functioning, whilestill allowing the golfer to record motion data and count shotsassociated with the particular club. For embodiments of the inventionthat utilize a mobile device (or more than one mobile device) withoutcamera(s), sensor data may be utilized to generate displays of thecaptured motion data, while the mobile device may optionally obtainimages from other cameras or other mobile devices with cameras. Forexample, display types that may or may not utilize images of the usermay include ratings, calculated data and time line data. Ratingsassociated with the captured motion can also be displayed to the user inthe form of numerical or graphical data with or without a user image,for example an “efficiency” rating. Other ratings may include linearacceleration and/or rotational acceleration values for the determinationof concussions and other events for example. Calculated data, such as apredicted ball flight path data can be calculated and displayed on themobile device with or without utilizing images of the user's body. Datadepicted on a time line can also be displayed with or without images ofthe user to show the relative peaks of velocity for various parts of theequipment or user's body for example. Images from multiple camerasincluding multiple mobile devices, for example from a crowd of golffans, may be combined into a BULLET TIME® visual effect characterized byslow motion of the golf swing shown from around the golfer at variousangles at normal speed. All analyzed data may be displayed locally, oruploaded to the database along with the motion capture data,images/videos, shot count and location data where it may undergo datamining processes, wherein the system may charge a fee for access to theresults for example.

In one or more embodiments, a user may play a golf course or hit tennisballs, or alternatively simply swing to generate motion capture data forexample and when wearing virtual reality glasses, see an avatar ofanother user, whether virtual or real in an augmented realityenvironment. In other embodiments, the user moves a piece of equipmentassociated with any sport or simply move the user's own body coupledwith motion capture sensors and view a virtual reality environmentdisplayed in virtual reality glasses of the user's movement or movementof a piece of equipment so instrumented. Alternatively or incombination, a virtual reality room or other environment may be utilizedto project the virtual reality avatars and motion data. Hence,embodiments of the system may allow a user on a real golf course to playalong with another user at a different location that is not actuallyhitting balls along with a historical player whose motion data has beenanalyzed or a data mining constructed user based on one or more motioncapture data sequences, and utilized by an embodiment of the system toproject an avatar of the historical player. Each of the three playersmay play in turn, as if they were located in the same place.

Motion capture data and/or events can be displayed in many ways, forexample tweeted, to a social network during or after motion capture. Forexample, if a certain amount of exercise or motion is performed, orcalories performed, or a new sports power factor maximum has beenobtained, the system can automatically tweet the new information to asocial network site so that anyone connected to the Internet may benotified. Motion capture data, motion analyses, and videos may betransmitted in one or more embodiments to one or more social mediasites, repositories, databases, servers, other computers, viewers,displays, other mobile devices, emergency services, or public agencies.The data uploaded to the Internet, i.e., a remote database or remoteserver or memory remote to the system may be viewed, analyzed or datamined by any computer that may obtain access to the data. This allowsfor remote compliance posting, e.g., tweeting and/or compliance and/ororiginal equipment manufacturers to determine for a given user whatequipment for compliance or sporting equipment for sports relatedembodiments is working best and/or what equipment to suggest. Datamining also enables suggestions for users to improve their complianceand/or the planning of sports venues, including golf courses based onthe data and/or metadata associated with users, such as age, or anyother demographics that may be entered into the system. Remote storageof data also enables medical applications such as morphologicalanalysis, range of motion over time, and diabetes prevention andexercise monitoring and compliance applications as stated. Otherapplications also allow for games that use real motion capture data fromother users, or historical players whether alive or dead after analyzingvideos of the historical players for example. Virtual reality andaugmented virtual reality applications may also utilize the motioncapture data or historical motion data. Military personnel such ascommanders and/or doctors may utilize the motion and/or images indetermine what type of G-forces a person has undergone from an explosionnear an Improvised Explosive Device and automatically route the besttype of medical aid automatically to the location of the motion capturesensor. One or more embodiments of the system may relay motion capturedata over a G-force or velocity threshold, to their commanding officeror nearest medical personnel for example via a wireless communicationlink. Alternatively, embodiments of the invention may broadcastlightweight connectionless concussion related messages to any mobiledevices listening, e.g., a referee's mobile phone to aid in theassistance of the injured player wherein the lightweight messageincludes an optional team/jersey number and an acceleration relatednumber such as a potential/probable concussion warning or indicator.

In one or more embodiments of the invention, fixed cameras such as at atennis tournament, football game, baseball game, car or motorcycle race,golf tournament or other sporting event can be utilized with acommunication interface located near the player/equipment having motioncapture elements so as to obtain, analyze and display motion capturedata. In this embodiment, real-time or near real-time motion data can bedisplayed on the video for augmented video replays. An increase in theentertainment level is thus created by visually displaying how fastequipment is moving during a shot, for example with rings drawn around aplayers hips and shoulders. Embodiments of the invention also allowimages or videos from other players having mobile devices to be utilizedon a mobile device related to another user so that users don't have toswitch mobile phones for example. In one embodiment, a video obtained bya first user for a piece of sporting equipment in motion that is notassociated with the second user having the video camera equipped mobilephone may automatically transfer the video to the first user for displaywith motion capture data associated with the first user. Video andimages may be uploaded into the database and data mined through imageanalysis to determine the types/colors of clothing or shoes for examplethat users are wearing.

Based on the display of data, the user can determine the equipment thatfits the best and immediately purchase the equipment, via the mobiledevice. For example, when deciding between two sets of skis, a user maytry out both pairs that are instrumented with motion capture elementswherein the motion capture data is analyzed to determine which pair ofskis enables more efficient movement. For golf embodiments, whendeciding between two golf clubs, a user can take swings with differentclubs and based on the analysis of the captured motion data andquantitatively determine which club performs better. Custom equipmentmay be ordered through an interface on the mobile device from a vendorthat can assemble-to-order customer built equipment and ship theequipment to the user for example. Shaft lengths for putters for examplethat are a standard length can be custom made for a particular userbased on captured motion data as a user putts with an adjustable lengthshaft for example. Based on data mining of the motion capture data andshot count data and distances for example allows for users havingsimilar swing characteristics to be compared against a current userwherein equipment that delivers longer shots for a given swing velocityfor a user of a particular size and age for example may be suggested orsearched for by the user to improve performance. OEMs may determine thatfor given swing speeds, which make and model of club delivers the bestoverall performance as well. One skilled in the art will recognize thatthis applies to all activities involving motion, not just golf.

Embodiments of the system may utilize a variety of sensor types. In oneor more embodiments of the invention, active sensors may integrate witha system that permits passive or active visual markers to be utilized tocapture motion of particular points on a user's body or equipment. Thismay be performed in a simply two-dimensional manner or in athree-dimensional manner if the mobile device includes two or morecameras, or if multiple cameras or mobile devices are utilized tocapture images such as video and share the images in order to createtriangulated three-dimensional motion data from a set of two-dimensionalimages obtained from each camera. Another embodiment of the inventionmay utilize inertial measurement units (IMU) or any other sensors thatcan produce any combination of weight, balance, posture, orientation,position, velocity, friction, acceleration, angular velocity and/orangular acceleration information to the mobile device. The sensors maythus obtain data that may include any combination of one or more valuesassociated with orientation (vertical or North/South or both), position(either via through Global Positioning System, i.e., “GPS” or throughtriangulation), linear velocity (in all three axes), angular velocity(e.g., from a gyroscope), linear acceleration (in all three axes) (e.g.,from an accelerometer), and angular acceleration. All motion capturedata obtained from the various sensor types may be saved in a databasefor analysis, monitoring, compliance, game playing or other use and/ordata mining, regardless of the sensor type.

In one or more embodiments of the invention, a sensor may be utilizedthat includes a passive marker or active marker on an outside surface ofthe sensor, so that the sensor may also be utilized for visual tracking(either two-dimensional or three-dimensional) and for orientation,position, velocity, acceleration, angular velocity, angular accelerationor any other physical quantity produced by the sensor. Visual markerembodiments of the motion capture element(s) may be passive or active,meaning that they may either have a visual portion that is visuallytrackable or may include a light emitting element such as a lightemitting diode (LED) that allows for image tracking in low lightconditions. This for example may be implemented with a graphical symbolor colored marker at the end of the shaft near the handle or at theopposing end of the golf club at the head of the club. Images or videosof the markers may be analyzed locally or saved in the database andanalyzed and then utilized in data mining. In addition, for concussionrelated embodiments, the visual marker may emit a light that isindicative of a concussion, for example flashing yellow for a moderateconcussion and fast flashing red for a sever concussion or any othervisual or optional audio event indicators or both. As previouslydiscussed, an LCD may output a local visual encoded message so that itis not intercepted or otherwise readable by anyone not having a mobiledevice local and equipped to read the code. This enables sensitivemedical messages to only be read by a referee or local medical personnelfor a concussion or paralysis related event for example.

Embodiments of the motion capture sensors may be generally mounted on ornear one or more end or opposing ends of sporting equipment, for examplesuch as a golf club and/or anywhere in between (for EI measurements) andmay integrate with other sensors coupled to equipment, such as weapons,medical equipment, wristbands, shoes, pants, shirts, gloves, clubs,bats, racquets, balls, helmets, caps, mouthpieces, etc., and/or may beattached to a user in any possible manner. For example, a rifle todetermine where the rifle was pointing when a recoil was detected by themotion capture sensor. This data may be transmitted to a central server,for example using a mobile computer such as a mobile phone or otherdevice and analyzed for war games practice for example. In addition, oneor more embodiments of the sensor can fit into a weight port of a golfclub, and/or in the handle end of the golf club. Other embodiments mayfit into the handle of, or end of, a tennis racquet or baseball bat forexample. Embodiments that are related to safety or health monitoring maybe coupled with a cap, helmet, and/or mouthpiece or in any other type ofenclosure. One or more embodiments of the invention may also operatewith balls that have integrated sensors as well. One or more embodimentsof the mobile device may include a small mountable computer such as anIPOD® SHUFFLE® or IPOD® NANO® that may or may not have integrateddisplays, and which are small enough to mount on a shaft of a piece ofsporting equipment and not affect a user's swing. Alternatively, thesystem may calculate the virtual flight path of a ball that has come incontact with equipment moved by a player. For example, with a baseballbat or tennis racquet or golf club having a sensor integrated into aweight port of other portion of the end of the club striking the golfball and having a second sensor located in the tip of the handle of thegolf club, or in one or more gloves worn by the player, an angle ofimpact can be calculated for the club. By knowing the loft of the faceof the club, an angle of flight may be calculated for the golf ball. Inaddition, by sampling the sensor at the end of the club at a high enoughspeed to determine oscillations indicative of where on the face of theclub the golf ball was struck, a quality of impact may be determined.These types of measurements and the analysis thereof help an athleteimprove, and for fitting purposes, allow an athlete to immediatelypurchase equipment that fits correctly. Centering data may be uploadedto the database and data mined for patterns related to the bats,racquets or clubs with the best centering on average, or the lowesttorsion values for example on a manufacturer basis for productimprovement. Any other unknown patterns in the data that are discoveredmay also be presented or suggested to users or search on by users, orpaid for, for example by manufacturers or users.

One or more embodiments of the sensor may contain charging features suchas mechanical eccentric weight, as utilized in some watches known as“automatic” or “self-winding” watches, optionally including a smallgenerator, or inductive charging coils for indirect electromechanicalcharging of the sensor power supply. Other embodiments may utilize plugsfor direct charging of the sensor power supply or electromechanical ormicroelectromechanical (MEMS) based charging elements. Any other type ofpower micro-harvesting technologies may be utilized in one or moreembodiments of the invention. One or more embodiments of the sensor mayutilize power saving features including gestures that power the sensoron or off. Such gestures may include motion, physical switches, contactwith the sensor, wired or wireless commands to the sensor, for examplefrom a mobile device that is associated with the particular sensors.Other elements that may couple with the sensor includes a battery, lowpower microcontroller, antenna and radio, heat sync, recharger andovercharge sensor for example. In addition, embodiments of the inventionallow for power down of some or all of the components of the systemuntil an electronic signal from accelerometers or a mechanical switchdetermines that the club has moved for example.

One or more embodiments of the invention enable Elasticity Inertia or EImeasurement of sporting equipment and even body parts for example.Placement of embodiments of the sensor along the shaft of a golf club,tennis racquet, baseball bat, hockey stick, shoe, human arm or any otheritem that is not perfectly stiff enables measurement of the amount offlex at points where sensors are located or between sensors. The angulardifferences in the each sensor over time allow for not only calculationof a flex profile, but also a flex profile that is dependent on time orforce. For example, known EI machines use static weights between tosupport points to determine an EI profile. These machines thereforecannot detect whether the EI profile is dependent upon the force appliedor is dependent on the time at which the force is applied, for exampleEI profiles may be non-linear with respect to force or time. Examplematerials that are known to have different physical properties withrespect to time include Maxwell materials and non-Newtonian fluids.

A user may also view the captured motion data in a graphical form on thedisplay of the mobile device or for example on a set of glasses thatcontains a video display. The captured motion data obtained fromembodiments of the motion capture element may also be utilized toaugment a virtual reality display of user in a virtual environment.Virtual reality or augmented reality views of patterns that are found inthe database via data mining are also in keeping with the spirit of theinvention. User's may also see augmented information such as an aimassist or aim guide that shows for example where a shot should beattempted to be placed for example based on existing wind conditions, orto account for hazards, e.g., trees that are in the way of a desireddestination for a ball, i.e., the golf hole for example.

One or more embodiments of the invention include a motion eventrecognition and video synchronization system that includes at least onemotion capture element that may couple with a user or piece of equipmentor mobile device coupled with the user. The at least one motion captureelement may include a memory, a sensor that may capture any combinationof values associated with an orientation, position, velocity,acceleration, angular velocity, and angular acceleration of the at leastone motion capture element, a communication interface, a microcontrollercoupled with the memory, the sensor and the communication interface. Inat least one embodiment, the microprocessor or microcontroller maycollect data that includes sensor values from the sensor, store the datain the memory, analyze the data and recognize an event within the datato determine event data, transmit the event data associated with theevent via the communication interface. The system may also include amobile device that includes a computer, a communication interface thatcommunicates with the communication interface of the motion captureelement to obtain the event data associated with the event, wherein thecomputer is coupled with computer's communication interface, wherein thecomputer may receive the event data from the computer's communicationinterface. The computer may also analyze the event data to form motionanalysis data, store the event data, or the motion analysis data, orboth the event data and the motion analysis data, obtain an event starttime and an event stop time from the event, request image data fromcamera that includes a video captured at least during a timespan fromthe event start time to the event stop time and display an event videoon a display that includes both the event data, the motion analysis dataor any combination thereof that occurs during the timespan from theevent start time to the event stop time and the video captured duringthe timespan from the event start time to the event stop time.

Embodiments may synchronize clocks in the system using any type ofsynchronization methodology and in one or more embodiments the computeron the mobile device is further configured to determine a clockdifference between the motion capture element and the mobile device andsynchronize the motion analysis data with the video. For example, one ormore embodiments of the invention provides procedures for multiplerecording devices to synchronize information about the time, location,or orientation of each device, so that data recorded about events fromdifferent devices can be combined. Such recording devices may beembedded sensors, mobile phones with cameras or microphones, or moregenerally any devices that can record data relevant to an activity ofinterest. In one or more embodiments, this synchronization isaccomplished by exchanging information between devices so that thedevices can agree on a common measurement for time, location, ororientation. For example, a mobile phone and an embedded sensor mayexchange messages with the current timestamps of their internal clocks;these messages allow a negotiation to occur wherein the two devicesagree on a common time. Such messages may be exchanged periodically asneeded to account for clock drift or motion of the devices after aprevious synchronization. In other embodiments, multiple recordingdevices may use a common server or set of servers to obtain standardizedmeasures of time, location, or orientation. For example, devices may usea GPS system to obtain absolute location information for each device.GPS systems may also be used to obtain standardized time. NTP (NetworkTime Protocol) servers may also be used as standardized time servers.Using servers allows devices to agree on common measurements withoutnecessarily being configured at all times to communicate with oneanother.

In one or more embodiments of the invention, some of the recordingdevices are configured to detect the occurrence of various events ofinterest. Some such events may occur at specific moments in time; othersmay occur over a time interval, wherein the detection includes detectionof the start of an event and of the end of an event. These devices areconfigured to record any combination of the time, location, ororientation of the recording device along with the event data, using thesynchronized measurement bases for time, location, and orientationdescribed above.

Embodiments of the computer on the mobile device may be furtherconfigured to discard at least a portion of the video outside of theevent start time to the event stop. For example, in one or moreembodiments of the invention, some of the recording devices capture datacontinuously to memory while awaiting the detection of an event. Toconserve memory, some devices may be configured to store data to a morepermanent local storage medium, or to a server, only when this data isproximate in time to a detected event. For example, in the absence of anevent detection, newly recorded data may ultimately overwrite previouslyrecorded data in memory. A circular buffer may be used in someembodiments as a typical implementation of such an overwriting scheme.When an event detection occurs, the recording device may store someconfigured amount of data prior to the start of the event, and someconfigured amount of data after the end of the event, in addition tostoring the data captured during the event itself. Any pre or post timeinterval is considered part of the event start time and event stop timeso that context of the event is shown in the video for example. Savingonly the video for the event on the mobile device with camera or cameraitself saves tremendous space and drastically reduces upload times.

Embodiments of the system may further comprise a server computer remoteto the mobile device and wherein the server computer is configured todiscard at least a portion of the video outside of the event start timeto the event stop and return the video captured during the timespan fromthe event start time to the event stop time to the computer in themobile device.

Embodiments of the at least one motion capture element may be configuredto transmit the event to at least one other motion capture sensor or atleast one other mobile device or any combination thereof, and whereinthe at least one other motion capture sensor or the at least one othermobile device or any combination thereof is configured to save dataassociated with said event. For example, in embodiments with multiplerecording devices operating simultaneously, one such device may detectan event and send a message to other recording devices that such anevent detection has occurred. This message can include the timestamp ofthe start and/or stop of the event, using the synchronized time basisfor the clocks of the various devices. The receiving devices, e.g.,other motion capture sensors and/or cameras may use the event detectionmessage to store data associated with the event to nonvolatile storageor to a server. The devices may be configured to store some amount ofdata prior to the start of the event and some amount of data after theend of the event, in addition to the data directly associated with theevent. In this way, all devices can record data simultaneously, but usean event trigger from only one of the devices to initiate saving ofdistributed event data from multiple sources.

Embodiments of the computer may be further configured to save the videofrom the event start time to the event stop time with the motionanalysis data that occurs from the event start time to the event stoptime or a remote server may be utilized to save the video. In one ormore embodiments of the invention, some of the recording devices may notbe in direct communication with each other throughout the time period inwhich events may occur. In these situations, devices can be configuredto save complete records of all of the data they have recorded topermanent storage or to a server. Saving of only data associated withevents may not be possible in these situations because some devices maynot be able to receive event trigger messages. In these situations,saved data can be processed after the fact to extract only the relevantportions associated with one or more detected events. For example,multiple mobile devices might record video of a player or performer, andupload this video continuously to a server for storage. Separately theplayer or performer may be equipped with an embedded sensor that is ableto detect events such as particular motions or actions. Embedded sensordata may be uploaded to the same server either continuously or at alater time. Since all data, including the video streams as well as theembedded sensor data, is generally timestamped, video associated withthe events detected by the embedded sensor can be extracted and combinedon the server.

Embodiments of the server or computer may be further configured while acommunication link is open between the at least one motion capturesensor and the mobile device to discard at least a portion of the videooutside of the event start time to the event stop and save the videofrom the event start time to the event stop time with the motionanalysis data that occurs from the event start time to the event stoptime. Alternatively, if the communication link is not open, embodimentsof the computer may be further configured to save video and after theevent is received after the communication link is open, then discard atleast a portion of the video outside of the event start time to theevent stop and save the video from the event start time to the eventstop time with the motion analysis data that occurs from the event starttime to the event stop time. For example, in some embodiments of theinvention, data may be uploaded to a server as described above, and thelocation and orientation data associated with each device's data streammay be used to extract data that is relevant to a detected event. Forexample, a large set of mobile devices may be used to record video atvarious locations throughout a golf tournament. This video data may beuploaded to a server either continuously or after the tournament. Afterthe tournament, sensor data with event detections may also be uploadedto the same server. Post-processing of these various data streams canidentify particular video streams that were recorded in the physicalproximity of events that occurred and at the same time. Additionalfilters may select video streams where a camera was pointing in thecorrect direction to observe an event. These selected streams may becombined with the sensor data to form an aggregate data stream withmultiple video angles showing an event.

The system may obtain video from a camera coupled with the mobiledevice, or any camera that is separate from or otherwise remote from themobile device. In one or more embodiments, the video is obtained from aserver remote to the mobile device, for example obtained after a queryfor video at a location and time interval.

Embodiments of the server or computer may be configured to synchronizesaid video and said event data, or said motion analysis data via imageanalysis to more accurately determine a start event frame or stop eventframe in said video or both, that is most closely associated with saidevent start time or said event stop time or both. In one or moreembodiments of the invention, synchronization of clocks betweenrecording devices may be approximate. It may be desirable to improve theaccuracy of synchronizing data feeds from multiple recording devicesbased on the view of an event from each device. In one or moreembodiments, processing of multiple data streams is used to observesignatures of events in the different streams to assist withfine-grained synchronization. For example, an embedded sensor may besynchronized with a mobile device including a video camera, but the timesynchronization may be accurate only to within 100 milliseconds. If thevideo camera is recording video at 30 frames per second, the video framecorresponding to an event detection on the embedded sensor can only bedetermined within 3 frames based on the synchronized timestamps alone.In one embodiment of the device, video frame image processing can beused to determine the precise frame corresponding most closely to thedetected event. For instance, a shock from a snowboard hitting theground that is detected by an inertial sensor may be correlated with theframe at which the geometric boundary of the snowboard makes contactwith the ground. Other embodiments may use other image processingtechniques or other methods of detecting event signatures to improvesynchronization of multiple data feeds.

Embodiments of the at least one motion capture element may include alocation determination element configured to determine a location thatis coupled with the microcontroller and wherein the microcontroller isconfigured to transmit the location to the computer on the mobiledevice. In one or more embodiments, the system further includes a serverwherein the microcontroller is configured to transmit the location tothe server, either directly or via the mobile device, and wherein thecomputer or server is configured to form the event video from portionsof the video based on the location and the event start time and theevent stop time. For example, in one or more embodiments, the eventvideo may be trimmed to a particular length of the event, and transcodedto any or video quality, and overlaid or otherwise integrated withmotion analysis data or event data, e.g., velocity or acceleration datain any manner. Video may be stored locally in any resolution, depth, orimage quality or compression type to store video or any other techniqueto maximize storage capacity or frame rate or with any compression typeto minimize storage, whether a communication link is open or not betweenthe mobile device, at least one motion capture sensor and/or server. Inone or more embodiments, the velocity or other motion analysis data maybe overlaid or otherwise combined, e.g., on a portion beneath the video,that includes the event start and stop time, that may include any numberof seconds before and/or after the actual event to provide video of theswing before a ball strike event for example. In one or moreembodiments, the at least one motion capture sensor and/or mobiledevice(s) may transmit events and video to a server wherein the servermay determine that particular videos and sensor data occurred in aparticular location at a particular time and construct event videos fromseveral videos and several sensor events. The sensor events may be fromone sensor or multiple sensors coupled with a user and/or piece ofequipment for example. Thus, the system may construct short videos thatcorrespond to the events, which greatly decreases video storagerequirements for example.

In one or more embodiments, the microcontroller or the computer isconfigured to determine a location of the event or the microcontrollerand the computer are configured to determine the location of the eventand correlate the location, for example by correlating or averaging thelocation to provide a central point of the event, and/or erroneouslocation data from initializing GPS sensors may be minimized. In thismanner, a group of users with mobile devices may generate videos of agolfer teeing off, wherein the event location of the at least one motioncapture device may be utilized and wherein the server may obtain videosfrom the spectators and generate an event video of the swing and ballstrike of the professional golfer, wherein the event video may utilizeframes from different cameras to generate a BULLET TIME® video fromaround the golfer as the golfer swings. The resulting video or videosmay be trimmed to the duration of the event, e.g., from the event starttime to the event stop time and/or with any pre or post predeterminedtime values around the event to ensure that the entire event is capturedincluding any setup time and any follow through time for the swing orother event.

In one or more embodiments, the computer on the mobile device mayrequest at least one image or video that contains the event from atleast one camera proximal to the event directly by broadcasting arequest for any videos taken in the area by any cameras, optionally thatmay include orientation information related to whether the camera wasnot only located proximally to the event, but also oriented or otherwisepointing at the event. In other embodiments, the video may be requestedby the computer on the mobile device from a remote server. In thisscenario, any location and/or time associated with an event may beutilized to return images and/or video near the event or taken at a timenear the event, or both. In one or more embodiments, the computer orserver may trim the video to correspond to the event duration and again,may utilize image processing techniques to further synchronize portionsof an event, such as a ball strike with the corresponding frame in thevideo that matches the acceleration data corresponding to the ballstrike on a piece of equipment for example.

Embodiments of the computer on the mobile device or on the server may beconfigured to display a list of one or more times at which an event hasoccurred or wherein one or more events has occurred. In this manner, auser may find events from a list to access the event videos in rapidfashion.

Embodiments of the invention may include at least one motion capturesensor that is physically coupled with said mobile device. Theseembodiments enable any type of mobile phone or camera system with anintegrated sensor, such as any type of helmet mounted camera or anymount that includes both a camera and a motion capture sensor togenerate event data and video data.

In some embodiments, the system may also include one or more computerswith a wireless communication interface that can communicate with theradios of one or more motion capture elements to receive the event dataassociated with motion events. The computer may receive raw motion data,and it may analyze this data to determine events. In other embodiments,the determination of events may occur in the motion capture element, andthe computer may receive event data. Combinations of these twoapproaches are also possible in some embodiments.

In some embodiments, the computer or computers may determine the starttime and end time of a motion event from the event data. They may thenrequest image data from a camera that has captured video or one or moreimages for some time interval at least within some portion of the timebetween this event start time and event end time. The term video in thisspecification will include individual images as well as continuousvideo, including the case of a camera that takes a single snapshot imageduring an event interval. This video data may then be associated withthe motion data form a synchronized event video. Events may be gesturedby a user by shaking or tapping a motion capture sensor a fixed numberof times for example. Any type of predefined event including usergesture events may be utilized to control at least one camera totransfer generally concise event videos without requiring the transferof huge raw video files.

In some embodiments, the request of video from a camera may occurconcurrently with the capture or analysis of motion data. In suchembodiments, the system will obtain or generate a notification that anevent has begun, and it will then request that video be streamed fromone or more cameras to the computer until the end of the event isdetected. In other embodiments, the request of video may occur after acamera has uploaded its video records to another computer, such as aserver. In this case, the computer will request video from the serverrather than directly from the camera.

Various techniques may be used to perform synchronization of motion dataand video data. Such techniques include clock synchronization methodswell-known in the art, such as the network time protocol, that ensurethat all devices—motion capture elements, computer, and cameras—use acommon time base. In another technique, the computer may compare itsclock to an internal clock of the motion capture element and to aninternal clock of a camera, by exchanging packets containing the currenttime as registered by each device. Other techniques analyze motion dataand video data to align their different time bases for synchronization.For instance, a particular video frame showing a contact with a ball maybe aligned with a particular data frame from motion data showing a shockin an accelerometer; these frames can then be used effectively as keyframes, to synchronize the motion data and the video data. The combinedvideo data and motion data forms a synchronized event video with anintegrated record of an event.

In one or more embodiments, a computer configured to receive or processmotion data or video data may be a mobile device, including but notlimited to a mobile telephone, a smartphone, a tablet, a PDA, a laptop,a notebook, a camera, glasses having at least one camera, or any otherdevice that can be easily transported or relocated. In otherembodiments, such a computer may integrated into a camera, and inparticular it may be integrated into the camera from which video data isobtained. In other embodiments, such a computer may be a desktopcomputer or a server computer, including but not limited to virtualcomputers running as virtual machines in a data center or in acloud-based service. In some embodiments, the system may includemultiple computers of any of the above types, and these computers mayjointly perform the operations described in this specification. As willbe obvious to one skilled in the art, such a distributed network ofcomputers can divide tasks in many possible ways and can coordinatetheir actions to replicate the actions of a single centralized computerif desired. The term computer in this specification is intended to meanany or all of the above types of computers, and to include networks ofmultiple such computers acting together.

In one or more embodiments, the computer may obtain or create a sequenceof synchronized event videos. The computer may display a compositesummary of this sequence for a user to review the history of the events.For the videos associated with each event, in some embodiments thissummary may include one or more thumbnail images generated from thevideos. In other embodiments, the summary may include smaller selectionsfrom the full event video. The composite summary may also includedisplay of motion analysis or event data associated with eachsynchronized event video. In some embodiments, the computer may obtain ametric and display the value of this metric for each event. The displayof these metric values may vary in different embodiments. In someembodiments, the display of metric values may be a bar graph, linegraph, or other graphical technique to show absolute or relative values.In other embodiments color-coding or other visual effects may be used.In other embodiments, the numerical values of the metrics may be shown.Some embodiments may use combinations of these approaches.

In one or more embodiments, the computer may accept selection criteriafor a metric of interest associated with the motion analysis data orevent data of the sequence of events. For example, a user may providecriteria such as metrics exceeding a threshold, or inside a range, oroutside a range. Any criteria may be used that may be applied to themetric values of the events. In response to the selection criteria, thecomputer may display only the synchronized event videos or theirsummaries (such as thumbnails) that meet the selection criteria. As anexample, a user capturing golf swing event data may wish to see onlythose swings with the swing speed above 100 mph.

In some embodiments of the invention, the computer may sort and ranksynchronized event videos for display based on the value of a selectedmetric, in addition to the filtering based on selection criteria asdescribed above. Continuing the example above, the user capturing golfswing data may wish to see only those swings with swing speed above 100mph, sorted with the highest swing speed shown first.

In one or more embodiments, the computer may generate a highlight reelthat combines the video for events that satisfy selection criteria. Sucha highlight reel might include the entire video for the selected events,or a portion of the video that corresponds to the important moments inthe event as determined by the motion analysis. In some embodiments, thehighlight reel might include overlays of data or graphics on the videoor on selected frames showing the value of metrics from the motionanalysis. Such a highlight reel may be generated automatically for auser once the user indicates which events to include by specifyingselection criteria. In some embodiments, the computer may allow the userto edit the highlight reel to add or remove events, to lengthen orshorten the video shown for each event, to add or remove graphicoverlays for motion data, or to add special effects or soundtracks.

In embodiments with multiple camera, motion data and multiple videostreams may be combined into a single synchronized event video. Videosfrom multiple cameras may provide different angles or views of an event,all synchronized to motion data and to a common time base. In someembodiments one or more videos may be available on one or more computers(such as servers or cloud services) and may be correlated later withevent data. In these embodiments, a computer may search for storedvideos that were in the correct location and orientation to view anevent. The computer could then retrieve the appropriate videos andcombine them with event data to form a composite view of the event withvideo from multiple positions and angles.

In some embodiments, the computer may select a particular video from theset of possible videos associated with an event. The selected video maybe the best or most complete view of the event based on various possiblecriteria. In some embodiments, the computer may use image analysis ofeach of the videos to determine the best selection. For example, someembodiments may use image analysis to determine which video is mostcomplete in that the equipment or people of interest are least occludedor are most clearly visible. In some embodiments, this image analysismay include analysis of the degree of shaking of a camera during thecapture of the video, and selection of the video with the most stableimages. In some embodiments, a user may make the selection of apreferred video, or the user may assist the computer in making theselection by specifying the most important criteria.

In some embodiments event data from a motion capture element may be usedto send control messages to a camera that can record video for theevent. In embodiments with multiple cameras, control messages could bebroadcast or could be send to a set of cameras during the event. Thesecontrol messages may modify the video recording parameters based on thedata associated with the event, including the motion analysis data. Forexample, a camera may be on standby and not recording while there is noevent of interest in progress. A computer may await event data, and oncean event starts it may send a command to a camera to begin recording.Once the event has finished, the computer may then send a command to thecamera to stop recording. Such techniques can conserve camera power aswell as video memory.

More generally in some embodiments a computer may send control messagesto a camera or cameras to modify any relevant video recording parametersin response to event data or motion analysis data. These recordingparameters might for example include the frame rate, resolution, colordepth, color or grayscale, compression method, and compression qualityof the video, as well as turning recording on or off. As an example ofwhere this may be useful, motion analysis data may indicate when a useror piece of equipment is moving rapidly; the frame rate of a videorecording could be increased during periods of rapid motion in response,and decreased during periods of relatively slow motion. By using ahigher frame rate during rapid motion, the user can slow the motion downduring playback to observe high motion events in great detail. Thesetechniques can allow cameras to conserve video memory and to useavailable memory efficiently for events of greatest interest.

In some embodiments, the computer may accept a sound track, for examplefrom a user, and integrate this sound track into the synchronized eventvideo. This integration would for example add an audio sound trackduring playback of an event video or a highlight reel. Some embodimentsmay use event data or motion analysis data to integrate the sound trackintelligently into the synchronized event video. For example, someembodiments may analyze a sound track to determine the beats of thesound track based for instance on time points of high audio amplitude.The beats of the sound track may then be synchronized with the eventusing event data or motion analysis data. For example, such techniquesmight automatically speed up or slow down a sound track as the motion ofa user or object increases or decreases. These techniques provide a richmedia experience with audio and visual cues associated with an event.

In one or more embodiments, a computer is configured to playback asynchronized event video on one or more displays. These displays may bedirectly attached to the computer, or may be remote on other devices.Using the event data or the motion analysis data, the computer maymodify the playback to add or change various effects. Thesemodifications may occur multiple times during playback, or evencontinuously during playback as the event data changes. For instance,during periods of low motion the playback may occur at normal speed,while during periods of high motion the playback may switch to slowmotion to highlight the details of the motion. Modifications to playbackspeed may be made based on any observed or calculated characteristics ofthe event or the motion. For instance, event data may identifyparticular sub-events of interest, such as the striking of a ball,beginning or end of a jump, or any other interesting moments. Thecomputer may modify the playback speed to slow down playback as thesynchronized event video approaches these sub-events. This slowdowncould increase continuously to highlight the sub-event in fine detail.Playback could even be stopped at the sub-event and await input from theuser to continue. Playback slowdown could also be based on the value ofone or more metrics from the motion analysis data or the event data. Forexample, motion analysis data may indicate the speed of a movingbaseball bat or golf club, and playback speed could be adjustedcontinuously to be slower as the speed of such an object increases.Playback speed could be made very slow near the peak value of suchmetrics.

In other embodiments, modifications could be made to other playbackcharacteristics not limited to playback speed. For example, the computercould modify any or all of playback speed, image brightness, imagecolors, image focus, image resolution, flashing special effects, or useof graphic overlays or borders. These modifications could be made basedon motion analysis data, event data, sub-events, or any othercharacteristic of the synchronized event video. As an example, asplayback approaches a sub-event of interest, a flashing special effectcould be added, and a border could be added around objects of interestin the video such as a ball that is about to be struck by a piece ofequipment.

In embodiments that include a sound track, modifications to playbackcharacteristics can include modifications to the playbackcharacteristics of the sound track. For example, such modificationsmight include modifications to the volume, tempo, tone, or audio specialeffects of the sound track. For instance, the volume and tempo of asound track might be increased as playback approaches a sub-event ofinterest, to highlight the sub-event and to provide a more dynamicexperience for the user watching and listening to the playback.

In one or more embodiments, a computer may use image analysis of a videoto generate a metric from an object within the video. This metric mayfor instance measure some aspect of the motion of the object. Suchmetrics derived from image analysis may be used in addition to or inconjunction with metrics obtained from motion analysis of data frommotion sensors. In some embodiments image analysis may use any ofseveral techniques known in the art to locate the pixels associated withan object of interest. For instance, certain objects may be known tohave specific colors, textures, or shapes, and these characteristics canbe used to locate the objects in video frames. As an example, a tennisball may be known to be approximately round, yellow, and of textureassociate with the ball's materials. Using these characteristics imageanalysis can locate a tennis ball in a video frame. Using multiple videoframes the approximate speed of the tennis ball could be calculated. Forinstance, assuming a stationary or almost stationary camera, thelocation of the tennis ball in three-dimensional space can be estimatedbased on the ball's location in the video frame and based on its size.The location in the frame gives the projection of the ball's locationonto the image plane, and the size provides the depth of the ballrelative to the camera. By using the ball's location in multiple frames,and by using the frame rate that gives the time difference betweenframes, the ball's velocity can be estimated.

In one or more embodiments, the microcontroller coupled to a motioncapture element is configured to communicate with other motion capturesensors to coordinate the capture of event data. The microcontroller maytransmit a start of event notification to another motion capture sensorto trigger that other sensor to also capture event data. The othersensor may save its data locally for later upload, or it may transmitits event data via an open communication link to a computer while theevent occurs. These techniques provide a type of master-slavearchitecture where one sensor can act as a master and can coordinate anetwork of slave sensors.

In one or more embodiments, a computer may obtain sensor values fromother sensors in addition to motion capture sensors, where these othersensors are proximal to an event and provide other useful dataassociated with the event. For example, such other sensors may sensevarious combinations of physical, environmental, chemical, inertial andphysiological values, including but not limited to temperature,humidity, wind, elevation, light, sound and physiological metrics (suchas a heartbeat). The computer may retrieve these other values and savethem along with the event data and the motion analysis data to generatean extended record of the event during the timespan from the event startto the event stop.

In one or more embodiments, the system may include one or more sensorelements that measure orientation, position, motion or any desiredsensor value. Sensor values may include for example, without limitation,inertial sensor values that obtain values related to one or more oforientation, position, velocity, acceleration, angular velocity, angularacceleration, or physical, chemical, environmental or physiologicalsensors that obtain values related to one or more of electromagneticfield, temperature, humidity, wind, pressure, elevation, light, sound,or heart rate.

In one or more embodiments, any computer or computers of the system mayaccess or receive media information from one or more servers, and theymay use this media information in conjunction with sensor data to detectand analyze events. Media information may include for example, withoutlimitation, text, audio, image, and video information. The computer orcomputers may analyze the sensor data to recognize an event, and theymay analyze the media information to confirm the event. Alternatively,in one or more embodiments the computer or computers may analyze themedia information to recognize an event, and they may analyze the sensordata to confirm the event. One or more embodiments may analyze thecombination of sensor data from sensor elements and media informationfrom servers to detect, confirm, reject, characterize, measure, monitor,assign probabilities to, or analyze any type of event.

Media information may include for example, without limitation, one ormore of email messages, voice calls, voicemails, audio recordings, videocalls, video messages, video recordings, Tweets®, Instagrams®, textmessages, chat messages, postings on social media sites, postings onblogs, or postings on wikis. Servers providing media information mayinclude for example, without limitation, one or more of an email server,a social media site, a photo sharing site, a video sharing site, a blog,a wiki, a database, a newsgroup, an RSS server, a multimedia repository,a document repository, a text message server, and a Twitter® server.

One or more embodiments may combine the media information (such asvideo, text, images, or audio) obtained from servers with the sensordata or other information to generate integrated records of an event.For example, images or videos that capture an event, or commentaries onthe event, may be retrieved from social media sites, filtered,summarized, and combined with sensor data and analyses; the combinedinformation may then be reposted to social media sites as an integratedrecord of the event. The integrated event records may be curated tocontain only highlights or selected media, or they may be comprehensiverecords containing all retrieved media.

One or more embodiments may analyze media information by searching textfor key words or key phrases related to an event, by searching imagesfor objects in those images that are related to an event, or bysearching audio for sounds related to an event.

One or more embodiments of the system may obtain sensor data from asensor element, and may obtain additional sensor data from additionalsensors or additional computers. This additional sensor data may be usedto detect events or to confirm events. One or more embodiments mayemploy a multi-stage event detection procedure that uses sensor data todetect a prospective event, and then uses additional sensor data, ormedia information, or both, to determine if the prospective event is avalid event or is a false positive.

One or more embodiments may use information from additional sensors todetermine the type of an activity or the equipment used for an activity.For example, one or more embodiments may use temperature or altitudedata from additional sensors to determine if motion data is associatedwith a surfing activity on a surfboard (high temperature and lowaltitude) or with a snowboarding activity on a snowboard (lowtemperature and high altitude).

One or more embodiments of the system may receive sensor data fromsensors coupled to multiple users or multiple pieces of equipment. Theseembodiments may detect events that for example involve actions ofmultiple users that occur at related times, at related locations, orboth. For example, one or more embodiments may analyze sensor data todetect individual events associated with a particular user or aparticular piece of equipment, and may aggregate these individual eventsto search for collective events across users or equipment that arecorrelated in time or location. One or more embodiments may determinethat a collective event has occurred if the number of individual eventswithin a specified time and location range exceeds a threshold value.Alternatively, or in addition, one or more embodiments may generateaggregate metrics from sensor data associated with groups of individualusers or individual pieces of equipment. These embodiments may detectcollective events for example if one or more aggregate metrics exceedscertain threshold values. One or more embodiments may generate aggregatemetrics for subgroups of users in particular areas, or at particulartime ranges, to correlate sensor data from these users by time andlocation.

Embodiments of the invention may automatically generate or select onemore tags for events, based for example on analysis of sensor data.Event data with tags may be stored in an event database for subsequentretrieval and analysis. Tags may represent for example, withoutlimitation, activity types, players, timestamps, stages of an activity,performance levels, or scoring results.

One or more embodiments may also analyze media such as text, audio,images, or videos from social media sites or other servers to generate,modify, or confirm event tags. Media analyzed may include for example,without limitation, email messages, voice calls, voicemails, audiorecordings, video calls, video messages, video recordings, textmessages, chat messages, postings on social media sites, postings onblogs, or postings on wikis. Sources of media for analysis may includefor example, without limitation, an email server, a social media site, aphoto sharing site, a video sharing site, a blog, a wiki, a database, anewsgroup, an RSS server, a multimedia repository, a documentrepository, and a text message server. Analysis may include searching oftext for key words and phrases related to an event. Event tags and otherevent data may be published to social media sites or to other servers orinformation systems.

One or more embodiments may provide the capability for users to manuallyadd tags to events, and to filter or query events based on the automaticor manual tags. Embodiments of the system may generate a video highlightreel for a selected set of events matching a set of tags. One or moreembodiments may discard portions of video based on the event analysisand tagging; for example, analysis may indicate a time interval withsignificant event activity, and video outside this time interval may bediscarded, e.g., to save tremendous amounts of memory, and/or nottransferred to another computer to save significant time in uploadingthe relevant events without the non-event data for example.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other aspects, features and advantages of the ideasconveyed through this disclosure will be more apparent from thefollowing more particular description thereof, presented in conjunctionwith the following drawings wherein:

FIG. 1 illustrates an embodiment of the sensor and media event detectionand tagging system.

FIG. 1A illustrates a logical hardware block diagram of an embodiment ofthe computer.

FIG. 1B illustrates an architectural view of an embodiment of thedatabase utilized in embodiments of the system.

FIG. 1C illustrates a flow chart for an embodiment of the processingperformed by embodiments of the computers in the system as shown inFIGS. 1 and 1A.

FIG. 1D illustrates a data flow diagram for an embodiment of the system.

FIG. 1E illustrates a synchronization chart that details the shifting ofmotion event times and/or video event times to align correctly in time.

FIGS. 1F and 1G illustrate an embodiment of the system that enablesbroadcasting images with augmented motion data.

FIG. 1H shows an embodiment of the processing that occurs on thecomputer.

FIG. 2A illustrates a helmet based mount that surrounds the head of auser wherein the helmet based mount holds a motion capture sensor.

FIG. 2B illustrates a neck insert based mount that enables retrofittingexisting helmets with a motion capture sensor.

FIG. 3 illustrates a close-up of the mount of FIGS. 2A-B showing theisolator between the motion capture sensor and external portion of thehelmet.

FIG. 4A illustrates a top cross sectional view of the helmet, padding,cranium, and brain of a user. FIG. 4B illustrates a rotationalconcussion event for the various elements shown in FIG. 4.

FIG. 5 illustrates the input force to the helmet, G1, versus theobserved force within the brain and as observed by the sensor whenmounted within the isolator.

FIG. 6 illustrates the rotational acceleration values of the 3 axesalong with the total rotational vector amount along with video of theconcussion event as obtained from a camera and displayed with the motionevent data.

FIG. 7 illustrates a timeline display of a user along with peak andminimum angular speeds along the timeline shown as events along the timeline. In addition, a graph showing the lead and lag of the golf clubalong with the droop and drift of the golf club is shown in the bottomdisplay wherein these values determine how much the golf club shaft isbending in two axes as plotted against time.

FIG. 8 illustrates a sub-event scrub timeline that enables inputs nearthe start/stop points in time associated with sub-events to be scrolledto, played to or from, to easily enable viewing of sub-events.

FIG. 9 illustrates the relative locations along the timeline wheresub-events start and stop and the gravity associated with the start andstop times, which enable user inputs near those points to gravitate tothe start and stop times.

FIG. 10 illustrates an embodiment that utilizes a mobile device as themotion capture element and another mobile device as the computer thatreceives the motion event data and video of the first user event.

FIG. 11 illustrates an embodiment of the memory utilized to store datarelated to a potential event.

FIG. 12 shows a flow chart of an embodiment of the functionalityspecifically programmed into the microcontroller to determine whether aprospective event has occurred.

FIG. 13 illustrates a typical event signature or template, which iscompared to motion capture data to eliminate false positive events.

FIG. 14 illustrates an embodiment of the motion capture elementconfigured with optional LED visual indicator for local display andviewing of event related information and an optional LCD configured todisplay a text or encoded message associated with the event.

FIG. 15 illustrates an embodiment of templates characteristic of motionevents associated with different types of equipment and/or instrumentedclothing along with areas in which the motion capture sensor personalitymay change to more accurately or more efficiently capture dataassociated with a particular period of time and/or sub-event.

FIG. 16 illustrates an embodiment of a protective mouthpiece in frontview and at the bottom portion of the figure in top view, for example asworn in any contact sport such as, but not limited to soccer, boxing,football, wrestling or any other sport for example.

FIG. 17 illustrates an embodiment of the algorithm utilized by anycomputer in FIG. 1 that is configured to display motion images andmotion capture data in a combined format.

FIG. 18 illustrates an embodiment of the synchronization architecturethat may be utilized by one or more embodiments of the invention.

FIG. 19 illustrates the detection of an event by one of the motioncapture sensors, transmission of the event detection to other motioncapture sensors and/or cameras, saving of the event motion data andtrimming of the video to correspond to the event.

FIG. 20 illustrates the process of culling a video for event videos, andselection of a best video clip for an event period where multiplecameras captured videos of the same event, along with a selectedsequence of synchronized event videos based on a selected metric, alongwith event videos sorted by selection criteria.

FIG. 21 illustrates image analysis to select a particular event videobased on the degree of shaking of a camera during the capture of thevideo, and selection of the video with the most stable images.

FIG. 22 illustrates control messages sent to the camera or cameras tomodify the video recording parameters based on the data associated withthe event, including the motion analysis data, for example while theevent is occurring.

FIG. 23 illustrates an embodiment of variable speed playback usingmotion data.

FIG. 24 illustrates image analysis of a video to assist withsynchronization of the video with event data and motion analysis dataand/or determine a motion characteristic of an object in the video notcoupled with a motion capture sensor.

FIG. 25 illustrates an embodiment of the system that combines sensordata analysis with analysis of text, audio, images and video fromservers to detect an event.

FIG. 26 illustrates an embodiment that analyzes text to classify anevent; it uses a weighting factor for each event and keyword combinationto compute an event score from the keywords located in the analyzedtext.

FIG. 27 illustrates an embodiment that uses sensor data to determine aprospective event, (a collision), and uses analysis of media todetermine whether the prospective event is valid or is a false positive.

FIG. 28 illustrates an embodiment that collects data using a motionsensor, and uses data from additional sensors, a temperature sensor andan altitude sensor, to determine whether the activity generating themotion data was snowboarding or surfing.

FIG. 29 illustrates an embodiment that collects and correlates data froma large number of sensors to detect an event involving an entire groupof persons; the vertical motion of audience members standing up atapproximately the same time indicates a standing ovation event.

FIG. 30 illustrates an embodiment that collects motion sensor data froma group of users near a location, and analyzes an aggregate metric,average speed, to detect that a major incident has occurred at thatlocation.

FIG. 31 illustrates an embodiment that automatically adds tags to anevent based on analysis of sensor data, and stores the tags along withthe metrics and sensor data for the event in an event database.

FIG. 32 shows an illustrative user interface that supports filtering ofevents by tag values, adding manually selected tags to events, andgeneration of a highlight reel containing video for a selected set ofevents.

FIG. 33 illustrates an embodiment that analyzes social media postings togenerate tags for an event.

FIG. 34 illustrates an embodiment that discards a portion of a videocapture not related to an event, and saves the relevant portion of thevideo along with the event and the event tags.

FIG. 35 illustrates an embodiment that integrates sensor data from anyor all of a wide range of sensors with media captures of any type, toform integrated, curated, event records containing both data and media;these integrated event records may be posted to social media sites orservices.

FIG. 36 illustrates an embodiment that correlates media from a varietyof media networks with sensor data from a variety of sensors;information that matches in time and location, for example within timeranges and location ranges indicative of a potential event, and thatmeets criteria for relevant events, wherein embodiments of the inventionconfirm or otherwise determine events and create integrated eventrecords and/or determine valid events and/or invalid events or fake newsevents.

FIG. 37 illustrates an embodiment that analyzes sensor data from a userto generate suggestions or recommendations to the user, for example on asocial media site; suggestions may for example include suggested friendsor contacts, suggested equipment or purchases, and suggested events oractivities.

DETAILED DESCRIPTION OF THE INVENTION

An event detection, confirmation and publication system that integratessensor data and social media will now be described. In the followingexemplary description, numerous specific details are set forth in orderto provide a more thorough understanding of the ideas describedthroughout this specification. It will be apparent, however, to anartisan of ordinary skill that embodiments of ideas described herein maybe practiced without incorporating all aspects of the specific detailsdescribed herein. In other instances, specific aspects well known tothose of ordinary skill in the art have not been described in detail soas not to obscure the disclosure. Readers should note that althoughexamples of the innovative concepts are set forth throughout thisdisclosure, the claims, and the full scope of any equivalents, are whatdefine the invention.

FIG. 1 illustrates an embodiment of the event detection, confirmationand publication system that integrates sensor data and social media 100.Embodiments of the invention enable detection of events using sensorsincluding inertial or motion capture sensors as well as other sensorssuch as physical sensors, environmental sensors, chemical sensors andphysiological sensors, i.e., sensors that obtain one or more valuesassociated with electromagnetic field, temperature, humidity, wind,pressure, elevation, light, sound, heart rate, etc., to detect, confirmevents, and/or publish or post events, or differentiate similar types ofmotion events to determine the type of equipment or activity or qualityof the event, such as how proficient a user is at a certain activity.Embodiments enable motion capture data and other sensor data to beutilized to curate text, sound, images, or 360 images, video, or 360video, and post the results to social networks, for example on a user'sor multiple user's timeline(s) or in a dedicated feed. One or moreembodiments may create integrated, curated records of an event bycombining sensor data with media retrieved from social media postings.Embodiments of the system also may post or filter to social media sitesfor example using any other filter besides location and time and thetext in the social media posts for example. Embodiments may also usemotion or other sensor data to define and event, eliminate falsepositive events, post true events, and/or correlate the events withsocial media to confirm the events, or post the events in a particularchannel for example. The system may use the combination of sensor dataand media for example from social media sites to not only confirm eventsand curate media to provide concise versions of the events, but alsodetermine whether an event is valid or invalid or represents fake news.One or more embodiments may be utilized to analyze multiple social mediaposts, or threads that are unknown across “friends” to determine eventsand/or provide emergency notifications to for example flash all mobiledevice screens in case of a local emergency or terrorist attack.

Embodiments also enable event based viewing and low power transmissionof events and communication with an app executing on a mobile deviceand/or with external cameras to designate windows that define theevents. Enables recognition of motion events, and designation of eventswithin images or videos, such as a shot, move or swing of a player, aconcussion of a player, boxer, rider or driver, or a heat stroke,hypothermia, seizure, asthma attack, epileptic attack or any othersporting or physical motion related event including walking and falling.Events may be correlated with one or more images or video as capturedfrom internal/external camera or cameras or nanny cam, for example toenable saving video of the event, such as the first steps of a child,violent shaking events, sporting events including concussions, orfalling events associated with an elderly person. As shown, embodimentsof the system generally include a mobile device 101 and applicationsthat execute thereon, that includes computer 160, shown as locatedinternally in mobile device 101 as a dotted outline, (i.e., also seefunctional view of computer 160 in FIG. 1A), display 120 coupled tocomputer 160 and a wireless communications interface (generally internalto the mobile device, see element 164 in FIG. 1A) coupled with thecomputer. In one or more embodiments, mobile device 101 may be forexample, without limitation, a smart phone, a mobile phone, a laptopcomputer, a notebook computer, a tablet computer, a personal digitalassistant, a music player, smart glasses having at least one camera, ora smart watch (including for example an Apple Watch®). Since mobilephones having mobile computers are ubiquitous, users of the system maypurchase one or more motion capture elements and an application, a.k.a.,“app”, that they install on their pre-existing phone to implement anembodiment of the system. Motion capture capabilities are thus availableat an affordable price for any user that already owns a mobile phone,tablet computer, music player, etc., which has never been possiblebefore.

Each mobile device 101, 102, 102 a, 102 b may optionally include aninternal identifier reader 190, for example an RFID reader, or maycouple with an identifier reader or RFID reader (see mobile device 102)to obtain identifier 191. Alternatively, embodiments of the inventionmay utilize any wired or wireless communication technology in any of thedevices to communicate an identifier that identifies equipment 110 tothe system. Embodiments of the invention may also include any other typeof identifier coupled with the at least one motion capture sensor or theuser or the piece of equipment. In one or more embodiments, theidentifier may include a team and jersey number or student identifiernumber or license number or any other identifier that enables relativelyunique identification of a particular event from a particular user orpiece of equipment. This enables team sports or locations with multipleplayers or users to be identified with respect to the app that mayreceive data associated with a particular player or user. One or moreembodiments receive the identifier, for example a passive RFIDidentifier or MAC address or other serial number associated with theplayer or user and associate the identifier with the event data andmotion analysis data.

The system generally includes at least one sensor, which may be any typeof inertial, physical, chemical, environment or physiological sensor asshown in FIG. 35. For example, computer 101 may include an altimeter, orthermometer or obtain these values wirelessly. Sensor or smart watch 191may include a heart rate monitor or may obtain values from an internalmedical device wirelessly for example. In addition, embodiments mayinclude motion capture element 111 that couples with user 150 or withpiece of equipment 110, for example via mount 192, for example to a golfclub, or baseball bat, tennis racquet, hockey stick, weapon, stick,sword, snow board, surf board, skate board, or any other board or pieceof equipment for any sport, or other sporting equipment such as a shoe,belt, gloves, glasses, hat, or any other item. The at least one motioncapture element 111 may be placed at one end, both ends, or anywherebetween both ends of piece of equipment 110 or anywhere on user 150,e.g., on a cap, headband, helmet, mouthpiece or any combination thereof,and may also be utilized for EI measurements of any item. The motioncapture element may optionally include a visual marker, either passiveor active, and/or may include a sensor, for example any sensor capableof providing any combination of one or more values associated with anorientation (North/South and/or up/down), position, velocity,acceleration, angular velocity, and angular acceleration of the motioncapture element. The computer may obtain data associated with anidentifier unique to each piece of equipment 110, e.g., clothing, bat,etc., for example from an RFID coupled with club 110, i.e., identifier191, and optionally associated with the at least one motion captureelement, either visually or via a communication interface receiving datafrom the motion capture element, analyze the data to form motionanalysis data and display the motion analysis data on display 120 ofmobile device 101. Motion capture element 111 may be mounted on or nearthe equipment or on or near the user via motion capture mount 192.Motion capture element 111 mounted on a helmet for example may includean isolator including a material that is may surround the motion captureelement to approximate physical acceleration dampening of cerebrospinalfluid around the user's brain to minimize translation of linearacceleration and rotational acceleration of event data to obtain anobserved linear acceleration and an observed rotational acceleration ofthe user's brain. This lowers processing requirements on the motioncapture element microcontroller for example and enables low memoryutilization and lower power requirements for event based transmission ofevent data. The motion capture data from motion capture element 111, anydata associated with the piece of equipment 110, such as identifier 191and any data associated with user 150, or any number of such users 150,such as second user 152 may be stored in locally in memory, or in adatabase local to the computer or in a remote database, for exampledatabase 172 for example that may be coupled with a server. Data fromany sensor type, or event data from analysis of sensor data may bestored in database 172 from each user 150, 152 for example when anetwork or telephonic network link is available from motion captureelement 111 to mobile device 101 and from mobile device 101 to network170 or Internet 171 and to database 172. Data mining is then performedon a large data set associated with any number of users and theirspecific characteristics and performance parameters. For example, in agolf embodiment of the invention, a club ID is obtained from the golfclub and a shot is detected by the motion capture element. Mobilecomputer 101 stores images/video of the user and receives the motioncapture data for the events/hits/shots/motion and the location of theevent on the course and subsequent shots and determines any parametersfor each event, such as distance or speed at the time of the event andthen performs any local analysis and display performance data on themobile device. When a network connection from the mobile device tonetwork 170 or Internet 171 is available or for example after a round ofgolf, the images/video, motion capture data and performance data isuploaded to database 172, for later analysis and/or display and/or datamining. In one or more embodiments, users 151, such as originalequipment manufacturers pay for access to the database, for example viaa computer such as computer 105 or mobile computer 101 or from any othercomputer capable of communicating with database 172 for example vianetwork 170, Internet 171 or via website 173 or a server that forms partof or is coupled with database 172. Data mining may execute on database172, for example that may include a local server computer, or may be runon computer 105 or mobile device 101, 102, 102 a or 102 b and access astandalone embodiment of database 172 for example. Data mining resultsmay be displayed on mobile device 101, computer 105, televisionbroadcast or web video originating from camera 130, 130 a and 130 b, or104, or camera 103 or smart glasses 103 a, or accessed via website 173or any combination thereof.

One or more embodiments of the at least one motion capture element mayfurther include a light emitting element that may output light if theevent occurs. This may be utilized to display a potential, mild orsevere level of concussion on the outer portion of the helmet withoutany required communication to any external device for example. Differentcolors or flashing intervals may also be utilized to relay informationrelated to the event. Alternatively, or in combination, the at least onemotion capture element may further include an audio output element thatmay output sound if the event occurs or if the at least one motioncapture sensor is out of range of the computer or wherein the computermay display and alert if the at least one motion capture sensor is outof range of the computer, or any combination thereof. Embodiments of thesensor may also utilize an LCD that outputs a coded analysis of thecurrent event, for example in a Quick Response (QR) code or bar code forexample so that a referee may obtain a snapshot of the analysis code ona mobile device locally, and so that the event is not viewed in areadable form on the sensor or transmitted and intercepted by anyoneelse.

One or more embodiments of the system may utilize a mobile device thatincludes at least one camera 130, for example coupled to the computerwithin the mobile device. As such, smart glasses having at least onecamera are considered to be a “mobile device” herein. This allows forthe computer within mobile device 101 to command or instruct the camera130, or any other devices, the computer or any other computer, to obtainan image or images, for example of the user during an athletic movement.The image(s) of the user may be overlaid with displays and ratings tomake the motion analysis data more understandable to a human forexample. Alternatively, detailed data displays without images of theuser may also be displayed on display 120 or for example on the displayof computer 105. In this manner, two-dimensional images and subsequentdisplay thereof is enabled. If mobile device 101 contains two cameras,as shown in mobile device 102, i.e., cameras 130 a and 130 b, then thecameras may be utilized to create a three-dimensional data set throughimage analysis of the visual markers for example. This allows fordistances and positions of visual markers to be ascertained andanalyzed. Images and/or video from any camera in any embodiments of theinvention may be stored on database 172, for example associated withuser 150, for data mining purposes. In one or more embodiments of theinvention image analysis on the images and/or video may be performed todetermine make/models of equipment, clothes, shoes, etc., that isutilized, for example per age of user 150 or time of day of play, or todiscover any other pattern in the data. Cameras may have field of viewsF2 and F3 at locations L1, L2 and L3 for example, and the user may haverange of motion S, and dimensions L.

Alternatively, for embodiments of mobile devices that have only onecamera, multiple mobile devices may be utilized to obtaintwo-dimensional data in the form of images that is triangulated todetermine the positions of visual markers. In one or more embodiments ofthe system, mobile device 101 and mobile device 102 a share image dataof user 150 to create three-dimensional motion analysis data. Bydetermining the positions of mobile devices 101 and 102 (via positiondetermination elements such as GPS chips in the devices as is common, orvia cell tower triangulation and which are not shown for brevity but aregenerally located internally in mobile devices just as computer 160 is),and by obtaining data from motion capture element 111 for examplelocations of pixels in the images where the visual markers are in eachimage, distances and hence speeds are readily obtained as one skilled inthe art will recognize.

Camera 103 or smart glasses 103 a may also be utilized either for stillimages or as is now common, for video. In embodiments of the system thatutilize external cameras, any method of obtaining data from the externalcamera is in keeping with the spirit of the system including for examplewireless communication of the data, or via wired communication as whencamera 103 is docked with computer 105 for example, which then maytransfer the data to mobile device 101. Smart glasses 103 a may functionas a camera, video camera and/or display and has at least one cameratherein.

In one or more embodiments of the system, the mobile device on which themotion analysis data is displayed is not required to have a camera,i.e., mobile device 102 b may display data even though it is notconfigured with a camera. As such, mobile device 102 b may obtain imagesfrom any combination of cameras on mobile device 101, 102, 102 a, camera103, smart glasses 103 a and/or television camera 104 so long as anyexternal camera may communicate images to mobile device 102 b. Inaddition, in one or more embodiments, camera 103 and smart glasses 103 amay be utilized as the mobile device. Alternatively, no camera isrequired at all to utilize the system. See also FIG. 17.

For television broadcasts, motion capture element 111 wirelesslytransmits data that is received by antenna 106. The wireless sensor datathus obtained from motion capture element 111 is combined with theimages obtained from television camera 104 to produce displays withaugmented motion analysis data that can be broadcast to televisions,computers such as computer 105, mobile devices 101, 102, 102 a, 102 b orany other device that may display images. The motion analysis data canbe positioned on display 120 for example by knowing the location of acamera (for example via GPS information), and by knowing the directionand/or orientation that the camera is pointing so long as the sensordata includes location data (for example GPS information). In otherembodiments, visual markers or image processing may be utilized to lockthe motion analysis data to the image, e.g., the golf club head can betracked in the images and the corresponding high, middle and lowposition of the club can be utilized to determine the orientation ofuser 150 to camera 130 or 104 or 103 for example to correctly plot theaugmented data onto the image of user 150. By time stamping images andtime stamping motion capture data, for example after synchronizing thetimer in the microcontroller with the timer on the mobile device andthen scanning the images for visual markers or sporting equipment atvarious positions, simplified motion capture data may be overlaid ontothe images. Any other method of combining images from a camera andmotion capture data may be utilized in one or more embodiments of theinvention. Any other algorithm for properly positioning the motionanalysis data on display 120 with respect to a user (or any otherdisplay such as on computer 105) may be utilized in keeping with thespirit of the system. For example, when obtaining events or groups ofevents via the sensor, after the app receives the events and/or timeranges to obtain images, the app may request image data from that timespan from it's local memory, any other mobile device, any other type ofcamera that may be communicated with and/or post event locations/timesso that external camera systems local to the event(s) may provide imagedata for the times of the event(s).

One such display that may be generated and displayed on mobile device101 include a BULLET TIME® view using two or more cameras selected frommobile devices 101, 102, 102 a, camera 103, and/or television camera 104or any other external camera. In this embodiment of the system, thecomputer may obtain two or more images of user 150 and data associatedwith the at least one motion capture element (whether a visual marker orsensor), wherein the two or more images are obtained from two or morecameras and wherein the computer may generate a display that shows slowmotion of user 150 shown from around the user at various angles atnormal speed. Such an embodiment for example allows a group of fans tocreate their own BULLET TIME® shot of a golf pro at a tournament forexample. The shots may be sent to computer 105 and any image processingrequired may be performed on computer 105 and broadcast to a televisionaudience for example. In other embodiments of the system, the users ofthe various mobile devices share their own set of images, and or uploadtheir shots to a website for later viewing for example. Embodiments ofthe invention also allow images or videos from other players havingmobile devices to be utilized on a mobile device related to another userso that users don't have to switch mobile phones for example. In oneembodiment, a video obtained by a first user for a piece of equipment inmotion that is not associated with the second user having the videocamera mobile phone may automatically transfer the video to the firstuser for display with motion capture data associated with the firstuser. Alternatively, the first user's mobile phone may be utilized as amotion sensor in place of or in addition to motion capture element 111and the second user's mobile phone may be utilized to capture video ofthe first user while in motion. The first user may optionally gesture onthe phone, tap/shake, etc., to indicate that the second mobile phoneshould start/stop motion capture for example.

FIG. 1A shows an embodiment of computer 160. In computer 160 includesprocessor 161 that executes software modules, commonly also known asapplications, generally stored as computer program instructions withinmain memory 162. Display interface 163 drives display 120 of mobiledevice 101 as shown in FIG. 1. Optional orientation/position module 167may include a North/South or up/down orientation chip or both. In one ormore embodiments, the orientation/position module may include a locationdetermination element coupled with the microcontroller. This may includea GPS device for example. Alternatively, or in combination, the computermay triangulate the location in concert with another computer, or obtainthe location from any other triangulation type of receiver, or calculatethe location based on images captured via a camera coupled with thecomputer and known to be oriented in a particular direction, wherein thecomputer calculates an offset from the mobile device based on thedirection and size of objects within the image for example. Optionalsensors 168 may coupled with processor 161 via a wired or wireless link.Optional sensors may include for example, without limitation, motionsensors, inertial sensors, physical sensors, chemical sensors,physiological sensors, environmental sensors for example any type oftemperature sensors, humidity sensors, altitude sensors, pressuresensors, ultrasonic or optical rangefinders, magnetometers, heartbeatsensors, pulse sensors, breathing sensors, and any sensors of anybiological functions, etc. The sensors may obtain data from network 170,or provide sensor data to network 170. In addition, Processor 161 mayobtain data directly from sensors 168 or via the communicationsinterface. Optional sensors 168 may be utilized for example as anindicator of hypothermia or heat stroke alone or in combination with anymotion detected that may be indicative of shaking or unconsciousness forexample. Communication interface 164 may include wireless or wiredcommunications hardware protocol chips and/or an RFID reader or an RFIDreader may couple to computer 160 externally or in any other manner forexample. In one or more embodiments of the system communicationinterface may include telephonic and/or data communications hardware. Inone or more embodiments communication interface 164 may include a Wi-Fi™or other IEEE 802.11 device and/or BLUETOOTH® wireless communicationinterface or ZigBee® wireless device or any other wired or wirelesstechnology. BLUETOOTH® class 1 devices have a range of approximately 100meters, class 2 devices have a range of approximately 10 meters.BLUETOOTH® Low Power devices have a range of approximately 50 meters.Any network protocol or network media may be utilized in embodiments ofthe system so long as mobile device 101 and motion capture element 111can communicate with one another. Processor 161, main memory 162,display interface 163, communication interface 164 andorientation/position module 167 may communicate with one another overcommunication infrastructure 165, which is commonly known as a “bus”.Communications path 166 may include wired or wireless medium that allowsfor communication with other wired or wireless devices over network 170.Network 170 may communicate with Internet 171 and/or database 172.Database 172 may be utilized to save or retrieve images or videos ofusers, or motion analysis data, or users displayed with motion analysisdata in one form or another. The data uploaded to the Internet, i.e., aremote database or remote server or memory remote to the system may beviewed, analyzed or data mined by any computer that may obtain access tothe data. This allows for original equipment manufacturers to determinefor a given user what sporting equipment is working best and/or whatequipment to suggest. Data mining also enables the planning of golfcourses based on the data and/or metadata associated with users, such asage, or any other demographics that may be entered into the system.Remote storage of data also enables medical applications such asmorphological analysis, range of motion over time, and diabetesprevention and exercise monitoring and compliance applications. Datamining based applications also allow for games that use real motioncapture data from other users, one or more previous performances of thesame user, or historical players whether alive or dead after analyzingmotion pictures or videos of the historical players for example. Virtualreality and augmented virtual reality applications may also utilize themotion capture data or historical motion data. The system also enablesuploading of performance related events and/or motion capture data todatabase 172, which for example may be implemented as a socialnetworking site. This allows for the user to post or otherwise “tweet”high scores, or other metrics during or after play to notify everyone onthe Internet of the new event. For example, one or more embodimentsinclude at least one motion capture element 111 that may couple with auser or piece of equipment or mobile device coupled with the user,wherein the at least one motion capture element includes a memory, suchas a sensory data memory, a sensor that may capture any combination ofvalues associated with an orientation, position, velocity, acceleration,angular velocity, and angular acceleration of the at least one motioncapture element, one or more of a first communication interface and atleast one other sensor, and a microcontroller, or microprocessor,coupled with the memory, the sensor and the first communicationinterface. According to at least embodiment of the invention, themicrocontroller may be a microprocessor. The microcontroller, ormicroprocessor, may collect data that includes sensor values from thesensor, store the data in the memory, analyze the data and recognize anevent within the data to determine event data and transmit the eventdata associated with the event via the communication interface.Embodiments of the system may also include an application that mayexecute on a mobile device wherein the mobile device includes acomputer, a second communication interface that may communicate with thefirst communication interface of the motion capture element to obtainthe event data associated with the event. The computer is coupled withthe first communication interface wherein the computer executes theapplication or “app” to configure the computer to receive the event datafrom the communication interface, analyze the event data to form motionanalysis data, store the event data, or the motion analysis data, orboth the event data and the motion analysis data, and displayinformation including the event data, or the motion analysis data, orboth associated with the at least one user on a display.

FIG. 1B illustrates an architectural view of an embodiment of database172 utilized in embodiments of the system. As shown tables 180-186include information related to N number of users, M pieces of equipmentper user, P number of sensors per user or equipment, S number of sensordata per sensor, T number of patterns found in the other tables, Dnumber of data users, V videos, and K user measurements (size, range ofmotion, speed for particular body parts/joints). All tables shown inFIG. 1B are exemplary and may include more or less information asdesired for the particular implementation. Specifically, table 180includes information related to user 150 which may include data relatedto the user such as age, height, weight, sex, address or any other data.Table 181 include information related to M number of pieces of equipment110, which may include clubs, racquets, bats, shirts, pants, shoes,gloves, helmets, etc., for example the manufacturer of the equipment,model of the equipment, and type of the equipment. For example, in agolf embodiment, the manufacturer may be the name of the manufacturer,the model may be a name or model number and the type may be the clubnumber, i.e., 9 iron, the equipment ID may be identifier 191 in one ormore embodiments of the invention. Table 182 may include informationrelated to P number of sensors 111 on user 150 or equipment 110 ormobile computer 101. The sensors associated with user 150 may includeclothing, clubs, helmets, caps, headbands, mouthpieces, etc., thesensors associated with equipment 110 may for example be motion capturedata sensors, while the sensors associated with mobile computer 101 mayinclude sensors 167 for position/orientation and sensors 130 forimages/video for example. Table 183 may include information related to Snumber of sensor data per user per equipment, wherein the table mayinclude the time and location of the sensor data, or any other metadatarelated to the sensor data such as temperature, weather, humidity, asobtained locally via the temperature sensor shown in FIG. 1A, or viawired or wireless communications or in any other manner for example, orthe sensor data may include this information or any combination thereof.The table may also contain a myriad of other fields, such as ball type,i.e., in a golf embodiment the type of golf ball utilized may be savedand later data mined for the best performing ball types, etc. This tablemay also include an event type as calculated locally, for example apotential concussion event. Table 184 may include information related toF number of patterns that have been found in the data mining process forexample. This may include fields that have been searched in the varioustables with a particular query and any resulting related results. Anydata mining results table type may be utilized in one or moreembodiments of the invention as desired for the particularimplementation. This may include search results of any kind, includingEI measurements, which also may be calculated on computer 160 locally,or any other search value from simple queries to complex patternsearches. Table 185 may include information related to D number of datamining users 151 and may include their access type, i.e., full databaseor pattern table, or limited to a particular manufacturer, etc., thetable may also include payment requirements and/or receipts for the typeof usage that the data mining user has paid for or agreed to pay for andany searches or suggestions related to any queries or patterns found forexample. Any other schema, including object oriented databaserelationships or memory based data structures that allow for data miningof sensor data including motion capture data is in keeping with thespirit of the invention. Although exemplary embodiments for particularactivities are given, one skilled in the art will appreciate that anytype of motion based activity may be captured and analyzed byembodiments of the system using a motion capture element and app thatruns on a user's existing cell phone 101, 102 or other computer 105 forexample. Embodiments of the database may include V number of videos 179as held in table 186 for example that include the user that generatedthe video, the video data, time and location of the video. Other mediatypes may be held in the database, including text, audio, and imagedata. The fields are optional and in one or more embodiments, the videosmay be stored on any of the mobile devices in the system or anycombination of the mobile devices and server/DB 172. In one or moreembodiments, the videos may be broken into a subset of videos that areassociated with the “time” field of the sensor data table 183, whereinthe time field may include an event start time and event stop time. Inthis scenario, large videos may be trimmed into one or more smallerevent videos that correspond to generally smaller time windowsassociated with events of the event type held in table 183 to greatlyreduce video storage requirements of the system. Table 180 a may includeinformation related to K number of user measurements, for example oflengths, speeds, ranges of motion, or other measurements of userdimensions or movements over time.

There are a myriad of applications that benefit and which are enabled byembodiments of the system that provide for viewing and analyzing motioncapture data on the mobile computer or server/database, for example fordata mining database 172 by users 151. For example, users 151 mayinclude compliance monitors, including for example parents, children orelderly, managers, doctors, insurance companies, police, military, orany other entity such as equipment manufacturers that may data mine forproduct improvement. For example, in a tennis embodiment by searchingfor top service speeds for users of a particular size or age, or in agolf embodiment by searching for distances, i.e., differences insequential locations in table 183 based on swing speed in the sensordata field in table 183 to determine which manufacturers have the bestclubs, or best clubs per age or height or weight per user, or a myriadof other patterns. Other embodiments related to compliance enablemessages from mobile computer 101 or from server/database to begenerated if thresholds for G-forces, (high or zero or any otherlevels), to be sent to compliance monitors, managers, doctors, insurancecompanies, etc., as previously described. Users 151 may includemarketing personnel that determine which pieces of equipment certainusers own and which related items that other similar users may own, inorder to target sales at particular users. Users 151 may include medicalpersonnel that may determine how much movement a sensor for examplecoupled with a shoe, i.e., a type of equipment, of a diabetic child hasmoved and how much this movement relates to the average non-diabeticchild, wherein suggestions as per table 185 may include givingincentives to the diabetic child to exercise more, etc., to bring thechild in line with healthy children. Sports physicians, physiologists orphysical therapists may utilize the data per user, or search over alarge number of users and compare a particular movement of a user orrange of motion for example to other users to determine what areas agiven user can improve on through stretching or exercise and which rangeof motion areas change over time per user or per population and forexample what type of equipment a user may utilize to account for changesover time, even before those changes take place. Data mining motioncapture data and image data related to motion provides unique advantagesto users 151. Data mining may be performed on flex parameters measuredby the sensors to determine if sporting equipment, shoes, human bodyparts or any other item changes in flexibility over time or betweenequipment manufacturers or any combination thereof.

To ensure that analysis of user 150 during a motion capture includesimages that are relatively associated with the horizon, i.e., nottilted, the system may include an orientation module that executes oncomputer 160 within mobile device 101 for example. The computer mayprompt a user to align the camera along a horizontal plane based onorientation data obtained from orientation hardware within mobile device101. Orientation hardware is common on mobile devices as one skilled inthe art will appreciate. This allows the image so captured to remainrelatively level with respect to the horizontal plane. The orientationmodule may also prompt the user to move the camera toward or away fromthe user, or zoom in or out to the user to place the user within agraphical “fit box”, to somewhat normalize the size of the user to becaptured. Images may also be utilized by users to prove that they havecomplied with doctor's orders for example to meet certain motionrequirements.

Embodiments of the system may recognize the at least one motion captureelement associated with user 150 or piece of equipment 110 and associateat least one motion capture element 111 with assigned locations on user150 or piece of equipment 110. For example, the user can shake aparticular motion capture element when prompted by the computer withinmobile device 101 to acknowledge which motion capture element thecomputer is requesting an identity for. Alternatively, motion sensordata may be analyzed for position and/or speed and/or acceleration whenperforming a known activity and automatically classified as to thelocation of mounting of the motion capture element automatically, or byprompting the user to acknowledge the assumed positions. Sensors may beassociated with a particular player by team name and jersey number forexample and stored in the memory of the motion capture sensor fortransmission of events. Any computer shown in FIG. 1 may be utilized toprogram the identifier associated with the particular motion capturesensor in keeping with the spirit of the invention.

One or more embodiments of the computer in mobile device 101 may obtainat least one image of user 150 and display a three-dimensional overlayonto the at least one image of user 150 wherein the three-dimensionaloverlay is associated with the motion analysis data. Various displaysmay be displayed on display 120. The display of motion analysis data mayinclude a rating associated with the motion analysis data, and/or adisplay of a calculated ball flight path associated with the motionanalysis data and/or a display of a time line showing points in timealong a time axis where peak values associated with the motion analysisdata occur and/or a suggest training regimen to aid the user inimproving mechanics of the user. These filtered or analyzed data sensorresults may be stored in database 172, for example in table 183, or theraw data may be analyzed on the database (or server associated with thedatabase or in any other computer or combination thereof in the systemshown in FIG. 1 for example), and then displayed on mobile computer 101or on website 173, or via a television broadcast from camera 104 forexample. Data mining results may be combined in any manner with theunique displays of the system and shown in any desired manner as well.

Embodiments of the system may also present an interface to enable user150 to purchase piece of equipment 110 over the second communicationinterface of mobile device 101, for example via the Internet, or viacomputer 105 which may be implemented as a server of a vendor. Inaddition, for custom fitting equipment, such as putter shaft lengths, orany other custom sizing of any type of equipment, embodiments of thesystem may present an interface to enable user 150 to order a customerfitted piece of equipment over the second communication interface ofmobile device 101. Embodiments of the invention also enable mobiledevice 101 to suggest better performing equipment to user 150 or toallow user 150 to search for better performing equipment as determinedby data mining of database 172 for distances of golf shots per club forusers with swing velocities within a predefined range of user 150. Thisallows for real life performance data to be mined and utilized forexample by users 151, such as OEMs to suggest equipment to user 150, andbe charged for doing so, for example by paying for access to data miningresults as displayed in any computer shown in FIG. 1 or via website 173for example. In one or more embodiments of the invention database 172keeps track of OEM data mining and may bill users 151 for the amount ofaccess each of users 151 has purchased and/or used for example over agiving billing period. See FIG. 1B for example.

Embodiments of the system may analyze the data obtained from at leastone motion capture element and determine how centered a collisionbetween a ball and the piece of equipment is based on oscillations ofthe at least one motion capture element coupled with the piece ofequipment and display an impact location based on the motion analysisdata. This performance data may also be stored in database 172 and usedby OEMs or coaches for example to suggest clubs with higher probabilityof a centered hit as data mined over a large number of collisions forexample.

While FIG. 1A depicts a physical device, the scope of the systems andmethods set forth herein may also encompass a virtual device, virtualmachine or simulator embodied in one or more computer programs executingon a computer or computer system and acting or providing a computersystem environment compatible with the methods and processesimplementing the disclosed ideas. Where a virtual machine, process,device or otherwise performs substantially similarly to that of aphysical computer system of the system, such a virtual platform willalso fall within the scope of a system of the disclosure,notwithstanding the description herein of a physical system such as thatin FIG. 1A.

FIG. 1C illustrates a flow chart for an embodiment of the processingperformed and enabled by embodiments of the computers utilized in thesystem. In one or more embodiments of the system, a plurality of motioncapture elements are optionally calibrated at 301. In some embodimentsthis means calibrating multiple sensors on a user or piece of equipmentto ensure that the sensors are aligned and/or set up with the same speedor acceleration values for a given input motion. In other embodiments ofthe invention, this means placing multiple motion capture sensors on acalibration object that moves and calibrates the orientation, position,velocity, acceleration, angular velocity, angular acceleration or anycombination thereof at the same time. This step general includesproviding motion capture elements and optional mount (or alternativelyallowing a mobile device with motion capture sensing capabilities to beutilized), and an app for example that allows a user with an existingmobile phone or computer to utilize embodiments of the system to obtainmotion capture data, and potentially analyze and/or send messages basedthereon. In one or more embodiments, users may simply purchase a motioncapture element and an app and begin immediately using the system. Thesystem captures motion data with motion capture element(s) at 302,recognized any events within the motion capture data, i.e., a linearand/or rotational acceleration over a threshold indicative of aconcussion, or a successful skateboard trick, and eliminate falsepositives through use of multiple sensors to correlate data anddetermine if indeed a true event has occurred for example at 303, andsends the motion capture data to a mobile computer 101, 102 or 105 forexample, which may include an IPOD®, ITOUCH®, IPAD®, IPHONE®, ANDROID®Phone or any other type of computer that a user may utilize to locallycollect data at 304. In one or more embodiments, the sensor may transmitan event to any other motion capture sensor to start an event datastorage process on the other sensors for example. In other embodiments,the sensor may transmit the event to other mobile devices to signifythat videos for the event should be saved with unneeded portions of thevideo discarded for example, to enable the video to be trimmed eithernear the point in time of the event or at a later time. In one or moreembodiments, the system minimizes the complexity of the sensor andoffloads processing to extremely capable computing elements found inexisting mobile phones and other electronic devices for example. Thetransmitting of data from the motion capture elements to the user'scomputer may happen when possible, periodically, on an event basis, whenpolled, or in any other manner as will be described in various sectionsherein. This saves great amount of power compared to known systems thatcontinuously send raw data in two ways, first data may be sent in eventpackets, within a time window around a particular motion event whichgreatly reduces the data to a meaningful small subset of total raw data,and secondly the data may be sent less than continuously, or at definedtimes, or when asked for data so as to limit the total number oftransmissions. In one or more embodiments, the event may displayedlocally, for example with an LED flashing on the motion capture sensor111, for example yellow slow flashing for potential concussion or redfast flashing for probably concussion at 305. Alternatively, or incombination, the alert or event may be transmitted and displayed on anyother computer or mobile device shown in FIG. 1 for example.

The main intelligence in the system is generally in the mobile computeror server where more processing power may be utilized and so as to takeadvantage of the communications capabilities that are ubiquitous inexisting mobile computers for example. In one or more embodiments of thesystem, the mobile computer may optionally obtain an identifier from theuser or equipment at 306, or this identifier may be transmitted as partof step 305, such as a passive RFID or active RFID or other identifiersuch as a team/jersey number or other player ID, which may be utilizedby the mobile computer to determine what user has just been potentiallyinjured, or what weight as user is lifting, or what shoes a user isrunning with, or what weapon a user is using, or what type of activity auser is using based on the identifier of the equipment. The mobilecomputer may analyze the motion capture data locally at 307 (just as in303 or in combination therewith), and display, i.e., show or sendinformation such as a message for example when a threshold is observedin the data, for example when too many G-forces have been registered bya player, soldier or race car driver, or when not enough motion isoccurring (either at the time or based on the patterns of data in thedatabase as discussed below based on the user's typical motion patternsor other user's motion patterns for example.) In other embodiments, oncea user has performed a certain amount of motion, a message may be sentto safety or compliance monitor(s) at 307 to store or otherwise displaythe data, including for example referees, parents, children or elderly,managers, doctors, insurance companies, police, military, or any otherentity such as equipment manufacturers. The message may be an SMSmessage, or email, or tweet or any other type of electroniccommunication. If the particular embodiment is configured for remoteanalysis or only remote analysis, then the motion capture data may besent to the server/database at 308. If the implementation does notutilize a remote database, the analysis on the mobile computer is local.If the implementation includes a remote database, then the analysis maybe performed on the mobile computer or server/database or both at 309.Once the database obtains the motion capture data, then the data may beanalyzed and a message may be sent from the server/database tocompliance personnel or business entities as desired to display theevent alone or in combination or with respect to previous event dataassociated with the user or other users at 310, for example associatedwith video of the event having the user or an avatar of the user and forexample as compared with previous performance data of the user or otheruser.

Embodiments of the invention make use of the data from the mobilecomputer and/or server for gaming, morphological comparing, compliance,tracking calories burned, work performed, monitoring of children orelderly based on motion or previous motion patterns that vary during theday and night, safety monitoring for players, troops when G-forcesexceed a threshold or motion stops, local use of running, jumpingthrowing motion capture data for example on a cell phone includingvirtual reality applications that make use of the user's current and/orprevious data or data from other users, or play music or select a playlist based on the type of motion a user is performing or data mining.For example if motion is similar to a known player in the database, thenthat user's playlist may be sent to the user's mobile computer 101. Theprocessing may be performed locally so if the motion is fast, fast musicis played and if the motion is slow, then slow music may be played. Anyother algorithm for playing music based on the motion of the user is inkeeping with the spirit of the invention. Any use of motion capture dataobtained from a motion capture element and app on an existing user'smobile computer is in keeping with the spirit of the invention,including using the motion data in virtual reality environments to showrelative motion of an avatar of another player using actual motion datafrom the user in a previous performance or from another user including ahistorical player for example. Display of information is generallyperformed via three scenarios, wherein display information is based onthe user's motion analysis data or related to the user's piece ofequipment and previous data, wherein previous data may be from the sameuser/equipment or one or more other users/equipment. Under thisscenario, a comparison of the current motion analysis data with previousdata associated with this user/equipment allows for patterns to beanalyzed with an extremely cost effective system having a motion capturesensor and app. Under another scenario, the display of information is afunction of the current user's performance, so that the previous dataselected from the user or another user/equipment is based on the currentuser's performance. This enables highly realistic game play, for examplea virtual tennis game against a historical player wherein the swings ofa user are effectively responded to by the capture motion from ahistorical player. This type of realistic game play with actual databoth current and previously stored data, for example a user playingagainst an average pattern of a top 10 player in tennis, i.e., the speedof serves, the speed and angle of return shots, for a given input shotof a user makes for game play that is as realistic as is possible.Television images may be for example analyzed to determine swing speedsand types of shots taken by historical players that may no longer bealive to test one's skills against a master, as if the master was stillalive and currently playing the user. Compliance and monitoring by theuser or a different user may be performed in a third scenario withoutcomparison to the user's previous or other user's previous data whereinthe different user does not have access to or own for example the mobilecomputer. In other words, the mobile phone is associated with the userbeing monitored and the different user is obtaining information relatedto the current performance of a user for example wearing a motioncapture element, such as a baby, or a diabetes patient.

FIG. 1D illustrates a data flow diagram for an embodiment of the system.As shown motion capture data is sent from a variety of motion captureelements 111 on many different types of equipment 110 or associated withuser 150, for example on clothing, a helmet, headband, cap, mouthpieceor anywhere else coupled with the user. The equipment or user mayoptionally have an identifier 191 that enables the system to associate avalue with the motion, i.e., the weight being lifted, the type ofracquet being used, the type of electronic device being used, i.e., agame controller or other object such as baby pajamas associated withsecond user 152, e.g., a baby. In one or more embodiments, elements 191in the figure may be replaced or augmented with motion capture elements111 as one skilled in the art will appreciate. In one or moreembodiments of the system, mobile computer 101 receives the motioncapture data, for example in event form and for example on an eventbasis or when requested by mobile computer 101, e.g., after motioncapture elements 111 declares that there is data and turns on a receiverfor a fix amount of time to field requests so as to not waste power, andif no requests are received, then turn the receiver off for a period oftime. Once the data is in mobile computer 101, then the data isanalyzed, for example to take raw or event based motion capture data andfor example determine items such as average speed, etc., that are morehumanly understandable in a concise manner. The data may be stored,shown to the right of mobile computer 101 and then the data may bedisplayed to user 150, or 151, for example in the form of a monitor orcompliance text or email or on a display associated with mobile computer101 or computer 105. This enables users not associated with the motioncapture element and optionally not even the mobile computer potentiallyto obtain monitor messages, for example saying that the baby isbreathing slowly, or for example to watch a virtual reality match orperformance, which may include a user supplying motion capture datacurrently, a user having previously stored data or a historical player,such as a famous golfer, etc., after analysis of motion in video frompast tournament performance(s). In gaming scenarios, where the dataobtained currently, for example from user 150 or equipment 110, thedisplay of data, for example on virtual reality glasses may make use ofthe previous data from that user/equipment or another user/equipment torespond to the user's current motion data, i.e., as a function of theuser's input. The previous data may be stored anywhere in the system,e.g., in the mobile computer 101, computer 105 or on the server ordatabase 172 (see FIG. 1). The previous data may be utilized for exampleto indicate to user 151 that user 150 has undergone a certain number ofpotential concussion events, and therefore must heal for a particularamount of time before playing again. Insurance companies may demand suchcompliance to lower medical expenses for example. Video may be storedand retrieved from mobile device 101, computer 105 or as shown in FIG.1, on server or in database coupled with server 172 to form event videosthat include the event data and the video of the event shownsimultaneously for example on a display, e.g., overlaid or shown inseparate portions of the display of mobile computer 101 or computer 105generally.

FIG. 2A illustrates a helmet 110 a based mount that surrounds the head150 a of a user wherein the helmet based mount holds a motion capturesensor 111, for example as shown on the rear portion of the helmet. FIG.2B illustrates a neck insert based mount, shown at the bottom rearportion of the helmet, that enables retrofitting existing helmets with amotion capture sensor 111. In embodiments that include at least onemotion capture sensor that may be coupled with or otherwise worn nearthe user's head 150 a, the microcontroller, or microprocessor, maycalculate of a location of impact on the user's head. The calculation ofthe location of impact on the user's head is based on the physicalgeometry of the user's head and/or helmet. For example, if motioncapture element 111 indicates a rearward acceleration with no rotation(to the right in the figure as shown), then the location of impact maybe calculated by tracing the vector of acceleration back to thedirection of the outside perimeter of the helmet or user's head. Thisnon-rotational calculation effectively indicates that the line of forcepasses near or through the center of gravity of the user's head/helmet,otherwise rotational forces are observed by motion capture element 111.If a sideward vector is observed at the motion capture element 111, thenthe impact point is calculated to be at the side of the helmet/head andthrough the center of gravity. Hence, any other impact that does notimpart a rotational acceleration to the motion capture sensor over atleast a time period near the peak of the acceleration for example, orduring any other time period, may be assumed to be imparted in adirection to the helmet/head that passes through the center of gravity.Hence, the calculation of the point of impact is calculated as theintersection of the outer perimeter of the helmet/head that a vector offorce is detected and traversed backwards to the point of impact bycalculating the distance and angle back from the center of gravity. Forexample, if the acceleration vector is at 45 degrees with no rotation,then the point of impact is 45 degrees back from the center of gravityof the helmet/head, hence calculating the sine of 45, approximately 0.7multiplied by the radius of the helmet or 5 inches, results in an impactabout 3.5 inches from the front of the helmet. Alternatively, thelocation of impact may be kept in angular format to indicate that theimpact was at 45 degrees from the front of the helmet/head. Conversely,if rotational acceleration is observed without linear acceleration, thenthe helmet/head is rotating about the sensor. In this scenario, theforce required to rotate the brain passes in front of the center ofgravity and is generally orthogonal to a line defined as passing throughthe center of gravity and the sensor, e.g., a side impact, otherwisetranslation linear acceleration would be observed. In this case, thelocation of impact then is on the side of the helmet/head opposite thedirection of the acceleration. Hence, these two calculations of locationof impact as examples of simplified methods of calculations that may beutilized although any other vector based algorithm that takes intoaccount the mass of the head/helmet and the size of the head/helmet maybe utilized. One such algorithm may utilize any mathematical equationssuch as F=m*a, i.e., Force equal mass times acceleration, andTorque=r×F, where r is the position vector at the outer portion of thehead/helmet, X is the cross product and F is the Force vector, tocalculate the force vector and translate back to the outer perimeter ofthe helmet/head to calculate the Force vector imparted at that locationif desired. Although described with respect to a helmet, otherembodiments of the at least one motion capture sensor may be coupledwith a hat or cap, within a protective mouthpiece, using any type ofmount, enclosure or coupling mechanism. Similar calculations may beutilized for the hat/cap/mouthpiece to determine a location/direction ofimpact, linear or rotational forces from the accelerations or any otherquantities that may be indicative of concussion related events forexample. Embodiments may include a temperature sensor coupled with theat least one motion capture sensor or with the microcontroller forexample as shown in FIG. 1A. The temperature sensor may be utilizedalone or in combination with the motion capture element, for example todetermine if the body or head is shivering, i.e., indicative ofhypothermia, or if no movement is detected and the temperature forexample measure wirelessly or via a wire based temperature sensorindicates that the body or brain is above a threshold indicative of heatstroke.

Embodiments of the invention may also utilize an isolator that maysurround the at least one motion capture element to approximate physicalacceleration dampening of cerebrospinal fluid around the user's brain tominimize translation of linear acceleration and rotational accelerationof the event data to obtain an observed linear acceleration and anobserved rotational acceleration of the user's brain. Thus embodimentsdo not have to translate forces or acceleration values or any othervalues from the helmet based acceleration to the observed brainacceleration values and thus embodiments of the invention utilize lesspower and storage to provide event specific data, which in turnminimizes the amount of data transfer which yields lower transmissionpower utilization. Different isolators may be utilized on afootball/hockey/lacrosse player's helmet based on the type of paddinginherent in the helmet. Other embodiments utilized in sports wherehelmets are not worn, or occasionally worn may also utilize at least onemotion capture sensor on a cap or hat, for example on a baseballplayer's hat, along with at least one sensor mounted on a battinghelmet. Headband mounts may also be utilized in sports where a cap isnot utilized, such as soccer to also determine concussions. In one ormore embodiments, the isolator utilized on a helmet may remain in theenclosure attached to the helmet and the sensor may be removed andplaced on another piece of equipment that does not make use of anisolator that matches the dampening of a user's brain fluids.Embodiments may automatically detect a type of motion and determine thetype of equipment that the motion capture sensor is currently attachedto based on characteristic motion patterns associated with certain typesof equipment, i.e., surfboard versus baseball bat. In one or moreembodiments an algorithm that may be utilized to calculate the physicalcharacteristics of an isolator may include mounting a motion capturesensor on a helmet and mounting a motion capture sensor in a headform ina crash test dummy head wherein the motion capture sensor in theheadform is enclosed in an isolator. By applying linear and rotationalaccelerations to the helmet and observing the difference in valuesobtained by the helmet sensor and observed by the sensor in the headformfor example with respect to a sensor placed in a cadaver head within ahelmet, the isolator material of the best matching dampening value maybe obtained that most closely matches the dampening effect of a humanbrain.

FIG. 3 illustrates a close-up of the mount of FIGS. 2A-B showing theisolator between the motion capture sensor and external portion of thehelmet. Embodiments of the invention may obtain/calculate a linearacceleration value or a rotational acceleration value or both. Thisenables rotational events to be monitored for concussions as well aslinear accelerations. As shown, an external acceleration G1 may impart alower acceleration more associated with the acceleration observed by thehuman brain, namely G2 on sensor 111 by utilizing isolator 111 c withinsensor mount 111 b. This enables rotational events to be monitored forconcussions as well as linear accelerations. Other events may make useof the linear and/or rotational acceleration and/or velocity, forexample as compared against patterns or templates to not only switchsensor personalities during an event to alter the capturecharacteristics dynamically, but also to characterize the type ofequipment currently being utilized with the current motion capturesensor. This enables a single motion capture element purchase by a userto instrument multiple pieces of equipment or clothing by enabling thesensor to automatically determine what type of equipment or piece ofclothing the sensor is coupled to based on the motion captured by thesensor when compared against characteristic patterns or templates ofmotion.

FIG. 4A illustrates a top cross sectional view of the motion captureelement 111 mounted on helmet 110 a having padding 110 a 1 thatsurrounds cranium 401, and brain 402 of a user. FIG. 4B illustrates arotational concussion event for the various elements shown in FIG. 4. Asshown, different acceleration values may be imparted on the human brain402 and cranium 401 having center of gravity 403 and surrounded bypadding 110 a 1 in helmet 110 a. As shown, to move within a unit timeperiod, the front portion of the brain must accelerate at a higher rateG2 a, than the rear portion of the brain at G2 c or at G2 b at thecenter of gravity. Hence, for a given rotational acceleration valuedifferent areas of the brain may be affected differently. One or moreembodiments of the invention may thus transmit information not onlyrelated to linear acceleration, but also with rotational acceleration.

FIG. 5 illustrates the input force to the helmet, G1, e.g., as shown at500 g, versus the observed force within the brain G2, and as observed bythe sensor when mounted within the isolator and as confirmed with knownheadform acceleration measurement systems. The upper right graph showsthat two known headform systems confirm acceleration values observed byan isolator based motion capture element 111 shown in FIG. 4A withrespect to headform mounted accelerometers.

FIG. 6 illustrates the rotational acceleration values of the 3 axesalong with the total rotational vector amount along with video of theconcussion event as obtained from a camera and displayed with the motionevent data. In one or more embodiments, the acceleration values from agiven sensor may be displayed for rotational (as shown) or linearvalues, for example by double tapping a mobile device screen, or in anyother manner. Embodiments of the invention may transmit the event dataassociated with the event using a connectionless broadcast message. Inone or more embodiments, depending on the communication employed,broadcast messages may include payloads with a limited amount of datathat may be utilized to avoid handshaking and overhead of a connectionbased protocol. In other embodiments connectionless or connection basedprotocols may be utilized in any combination. In this manner, a refereemay obtain nearly instantaneous readouts of potential concussion relatedevents on a mobile device, which allows the referee to obtain medicalassistance in rapid fashion.

In one or more embodiments, the computer may access previously storedevent data or motion analysis data associated with at least one otheruser, or the user, or at least one other piece of equipment, or thepiece of equipment, for example to determine the number of concussionsor falls or other swings, or any other motion event. Embodiments mayalso display information including a presentation of the event dataassociated with the at least one user on a display based on the eventdata or motion analysis data associated with the user or piece ofequipment and the previously stored event data or motion analysis dataassociated with the user or the piece of equipment or with the at leastone other user or the other piece of equipment. This enables comparisonof motion events, in number or quantitative value, e.g., the maximumrotational acceleration observed by the user or other users in aparticular game or historically. In addition, in at least oneembodiment, patterns or templates that define characteristic motion ofparticular pieces of equipment for typical events may be dynamicallyupdated, for example on a central server or locally, and dynamicallyupdated in motion capture sensors via the first communication interfacein one or more embodiments. This enables sensors to improve over time.Hence, the display shown in FIG. 6 may also indicate the number ofconcussions previously stored for a given boxer/player and enable thereferee/doctor to make a decision as to whether or not the player maykeep playing or not.

Embodiments of the invention may transmit the information to a displayon a visual display coupled with the computer or a remote computer, forexample over broadcast television or the Internet for example. Hence,the display in FIG. 6 may be also shown to a viewing audience, forexample in real-time to indicate the amount of force imparted upon theboxer/player/rider, etc.

FIG. 7 illustrates a timeline display 2601 of a user along with peak andminimum angular speeds along the timeline shown as events along the timeline. In addition, a graph showing the lead and lag of the golf club2602 along with the droop and drift of the golf club is shown in thebottom display wherein these values determine how much the golf clubshaft is bending in two axes as plotted against time. An embodiment ofthe display is shown in FIG. 8 with simplified time line and motionrelated event (maximum speed of the swing) annotated on the display.

FIG. 8 illustrates a sub-event scrub timeline that enables inputs nearthe start/stop points 802 a-d in time, i.e., sub-event time locationsshown in FIG. 7 and associated with sub-events to be scrolled to, playedto or from, to easily enable viewing of sub-events. For example a golfswing may include sub-events such as an address, swing back, swingforward, strike, follow through. The system may display time locationsfor the sub-events 802 a-d and accept user input near the location toassert that the video should start or stop at that point in time, orscroll to or back to that point in time for ease of viewing sub-eventsfor example. User input element 801 may be utilized to drag the time toa nearby sub-event for example to position the video at a desired pointin time. Alternatively, or in combination a user input such as assertinga finger press near another sub-event point in time while the video isplaying, may indicate that the video should stop at the next sub-eventpoint in time. The user interface may also be utilized to control-dragthe points to more precisely synchronize the video to the frame in whicha particular sub-event or event occurs. For example, the user may holdthe control key and drag a point 802 b to the left or right to match theframe of the video to the actual point in time where the velocity of theclub head is zero for example to more closely synchronize the video tothe actual motion analysis data shown, here Swing Speed in miles perhour. Any other user gesture may be utilized in keeping with the spiritof the invention to synchronize a user frame to the motion analysisdata, such as voice control, arrow keys, etc.

FIG. 9 illustrates the relative locations along the timeline wheresub-events 802 a and 802 b start and stop and the gravity associatedwith the start and stop times, which enable user inputs near thosepoints to gravitate to the start and stop times. For example, whendragging the user interface element 801 left and right along the timeline, the user interface element may appear to move toward the potentialwell 802 a and 802 b, so that the user interface element is easier tomove to the start/stop point of a sub-event.

In one or more embodiments, the computer may request at least one imageor video that contains the event from at least one camera proximal tothe event. This may include a broadcast message requesting video from aparticular proximal camera or a camera that is pointing in the directionof the event. In one or more embodiments, the computer may broadcast arequest for camera locations proximal to the event or oriented to viewthe event, and optionally display the available cameras, or videostherefrom for the time duration around the event of interest. In one ormore embodiments, the computer may display a list of one or more timesat which the event has occurred, which enables the user obtain thedesired event video via the computer, and/or to independently requestthe video from a third party with the desired event times. The computermay obtain videos from the server 172 as well and locally trim the videoto the desired events. This may be utilized to obtain third party videosor videos from systems that do not directly interface with the computer,but which may be in communication with the server 172.

FIG. 10 illustrates an embodiment that utilizes a mobile device 102 b asthe motion capture element 111 a and another mobile device 102 a as thecomputer that receives the motion event data and video of the first userevent. The view from mobile device 102 a is shown in the left upperportion of the figure. In one or more embodiments, the at least onemotion capture sensor is coupled with the mobile device and for exampleuses an internal motion sensor 111 a within or coupled with the mobiledevice. This enables motion capture and event recognition with minimaland ubiquitous hardware, e.g., using a mobile device with a built-inaccelerometer. In one or more embodiments, a first mobile device 102 bmay be coupled with a user recording motion data, here shownskateboarding, while a second mobile device 102 a is utilized to recorda video of the motion. In one or more embodiments, the user undergoingmotion may gesture, e.g., tap N times on the mobile device to indicatethat the second user's mobile device should start recording video orstop recording video. Any other gesture may be utilized to communicateevent related or motion related indications between mobile devices.

Thus, embodiments of the invention may recognize any type of motionevent, including events related to motion that is indicative ofstanding, walking, falling, a heat stroke, seizure, violent shaking, aconcussion, a collision, abnormal gait, abnormal or non-existentbreathing or any combination thereof or any other type of event having aduration of time during with motion occurs. Events may also be of anygranularity, for example include sub-events that have known signatures,or otherwise match a template or pattern of any type, includingamplitude and/or time thresholds in particular sets of linear orrotational axes. For example, events indicating a skateboard push-off orseries of pushes may be grouped into a sub-event such as “prep formaneuver”, while rotational axes in X for example may indicate“skateboard flip/roll”. In one or more embodiments, the events may begrouped and stored/sent.

FIG. 11 illustrates an embodiment of the memory utilized to store data.Memory 4601 may for example be integral to the microcontroller in motioncapture element 111 or may couple with the microcontroller, as forexample a separate memory chip. Memory 4601 as shown may include one ormore memory buffer 4610, 4611 and 4620, 4621 respectively. Oneembodiment of the memory buffer that may be utilized is a ring buffer.The ring buffer may be implemented to be overwritten multiple timesuntil an event occurs. The length of the ring buffer may be from 0 to Nmemory units. There may for example be M ring buffers, for M strikeevents for example. The number M may be any number greater than zero. Inone or more embodiments, the number M may be equal to or greater thanthe number of expected events, e.g., number of hits, or shots for around of golf, or any other number for example that allows all motioncapture data to be stored on the motion capture element until downloadedto a mobile computer or the Internet after one or more events. In oneembodiment, a pointer, for example called HEAD keeps track of the headof the buffer. As data is recorded in the buffer, the HEAD is movedforward by the appropriate amount pointing to the next free memory unit.When the buffer becomes full, the pointer wraps around to the beginningof the buffer and overwrites previous values as it encounters them.Although the data is being overwritten, at any instance in time (t),there is recorded sensor data from time (t) back depending on the sizeof the buffer and the rate of recording. As the sensor records data inthe buffer, an “Event” in one or more embodiments stops new data fromoverwriting the buffer. Upon the detection of an Event, the sensor cancontinue to record data in a second buffer 4611 to record post Eventdata, for example for a specific amount of time at a specific capturerate to complete the recording of a prospective shot. Memory buffer 4610now contains a record of data for a desired amount of time from theEvent backwards, depending on the size of the buffer and capture ratealong with post Event data in the post event buffer 4611. Video may alsobe stored in a similar manner and later trimmed, see FIG. 19 forexample.

For example, in a golf swing, the event can be the impact of the clubhead with the ball. Alternatively, the event can be the impact of theclub head with the ground, which may give rise to a false event. Inother embodiments, the event may be an acceleration of a user's headwhich may be indicative of a concussion event, or a shot fired from aweapon, or a ball striking a baseball bat or when a user moves a weightto the highest point and descends for another repetition. The Pre-Eventbuffer stores the sensor data up to the event of impact, the Post-Eventbuffer stores the sensor data after the impact event. One or moreembodiments of the microcontroller, or microprocessor, may analyze theevent and determine if the event is a repetition, firing or event suchas a strike or a false strike. If the event is considered a valid eventaccording to a pattern or signature or template (see FIGS. 13 and 15),and not a false event, then another memory buffer 4620 is used formotion capture data up until the occurrence of a second event. Afterthat event occurs, the post event buffer 4621 is filled with captureddata.

Specifically, the motion capture element 111 may be implemented as oneor more MEMs sensors. The sensors may be commanded to collect data atspecific time intervals. At each interval, data is read from the variousMEMs devices, and stored in the ring buffer. A set of values read fromthe MEMs sensors is considered a FRAME of data. A FRAME of data can be0, 1, or multiple memory units depending on the type of data that isbeing collected and stored in the buffer. A FRAME of data is alsoassociated with a time interval. Therefore frames are also associatedwith a time element based on the capture rate from the sensors. Forexample, if each Frame is filled at 2 ms intervals, then 1000 FRAMESwould contain 2000 ms of data (2 seconds). In general, a FRAME does nothave to be associated with time.

Data can be constantly stored in the ring buffer and written out tonon-volatile memory or sent over a wireless or wired link over aradio/antenna to a remote memory or device for example at specifiedevents, times, or when communication is available over a radio/antennato a mobile device or any other computer or memory, or when commandedfor example by a mobile device, i.e., “polled”, or at any other desiredevent.

FIG. 12 shows a flow chart of an embodiment of the functionalityspecifically programmed into the microcontroller to determine whether anevent that is to be transmitted for the particular application, forexample a prospective event or for example an event has occurred. Themotion, acceleration or shockwave that occurs from an impact to thesporting equipment is transmitted to the sensor in the motion captureelement, which records the motion capture data as is described in FIG.11 above. The microcontroller, or microprocessor, may analyze the eventand determine whether the event is a prospective event or not.

One type of event that occurs is acceleration or ahead/helmet/cap/mouthpiece based sensor over a specified linear orrotational value, or the impact of the clubface when it impacts a golfball. In other sports that utilize a ball and a striking implement, thesame analysis is applied, but tailored to the specific sport andsporting equipment. In tennis, a prospective strike can be the racquethitting the ball, for example as opposed to spinning the racquet beforereceiving a serve. In other applications, such as running shoes, theimpact detection algorithm can detect the shoe hitting the ground whensomeone is running. In exercise, it can be a particular motion beingachieved, this allows for example the counting of repetitions whilelifting weights or riding a stationary bike.

In one or more embodiments of the invention, processing starts at 4701.The microcontroller compares the motion capture data in memory 4610 withlinear velocity over a certain threshold at 4702, within a particularimpact time frame and searches for a discontinuity threshold where thereis a sudden change in velocity or acceleration above a certain thresholdat 4703. If no discontinuity in velocity or for example accelerationoccurs in the defined time window, then processing continues at 4702. Ifa discontinuity does occur, then the prospective impact is saved inmemory and post impact data is saved for a given time P at 4704. Forexample, if the impact threshold is set to 12G, discontinuity thresholdis set to 6G, and the impact time frames is 10 frames, thenmicrocontroller 3802 signals impact, after detection of a 12Gacceleration in at least one axis or all axes within 10 frames followedby a discontinuity of 6G. In a typical event, the accelerations buildwith characteristic accelerations curves. Impact is signaled as a quickchange in acceleration/velocity. These changes are generally distinctfrom the smooth curves created by an incrementally increasing ordecreasing curves of a particular non-event. For concussion basedevents, linear or rotational acceleration in one or more axes is over athreshold. For golf related events, if the acceleration curves are thatof a golf swing, then particular axes have particular accelerations thatfit within a signature, template or other pattern and a ball strikeresults in a large acceleration strike indicative of a hit. If the datamatches a given template, then it is saved, if not, it processingcontinues back at 4702. If data is to be saved externally as determinedat 4705, i.e., there is a communication link to a mobile device and themobile device is polling or has requested impact data when it occurs forexample, then the event is transmitted to an external memory, or themobile device or saved externally in any other location at 4706 andprocessing continues again at 4702 where the microcontroller analyzescollected motion capture data for subsequent events. If data is not tobe saved externally, then processing continues at 4702 with the impactdata saved locally in memory 4601. If sent externally, the other motioncapture devices may also save their motion data for the event detectedby another sensor. This enables sensors with finer resolution or moremotion for example to alert other sensors associated with the user orpiece of equipment to save the event even if the motion capture datadoes not reach a particular threshold or pattern, for example see FIG.15. This type of processing provides more robust event detection asmultiple sensors may be utilized to detect a particular type of eventand notify other sensors that may not match the event pattern for onereason or another. In addition, cameras may be notified and trim orotherwise discard unneeded video and save event related video, which maylower memory utilization not only of events but also for video. In oneor more embodiments of the invention, noise may be filtered from themotion capture data before sending, and the sample rate may be variedbased on the data values obtained to maximize accuracy. For example,some sensors output data that is not accurate under high sampling ratesand high G-forces. Hence, by lowering the sampling rate at highG-forces, accuracy is maintained. In one or more embodiments of theinvention, the microcontroller associated with motion capture element111 may sense high G forces and automatically switch the sampling rate.In one or more embodiments, instead of using accelerometers with6G/12G/24G ranges or 2G/4G/8G/16G ranges, accelerometers with 2 ranges,for example 2G and 24G may be utilized to simplify the logic ofswitching between ranges.

One or more embodiments of the invention may transmit the event to amobile device and/or continue to save the events in memory, for examplefor a round of golf or until a mobile device communication link isachieved.

For example, with the sensor mounted in a particular mount, a typicalevent signature is shown in FIG. 13, also see FIG. 15 for comparison oftwo characteristic motion types as shown via patterns or templatesassociated with different pieces of equipment or clothing for example.In one or more embodiments, the microcontroller may execute a patternmatching algorithm to follow the curves for each of the axis and usesegments of 1 or more axis to determine if a characteristic swing hastaken place, in either linear or rotational acceleration or anycombination thereof. If the motion capture data in memory 4601 is withina range close enough to the values of a typical swing as shown in FIG.13, then the motion is consistent with an event. Embodiments of theinvention thus reduce the number of false positives in event detection,after first characterizing the angular and/or linear velocity signatureof the movement, and then utilizing elements of this signature todetermine if similar signatures for future events have occurred.

The motion capture element collects data from various sensors. The datacapture rate may be high and if so, there are significant amounts ofdata that is being captured. Embodiments of the invention may use bothlossless and lossy compression algorithms to store the data on thesensor depending on the particular application. The compressionalgorithms enable the motion capture element to capture more data withinthe given resources. Compressed data is also what is transferred to theremote computer(s). Compressed data transfers faster. Compressed data isalso stored in the Internet “in the cloud”, or on the database using upless space locally.

FIG. 14 illustrates an embodiment of the motion capture element 111 mayinclude an optional LED visual indicator 1401 for local display andviewing of event related information and an optional LCD 1402 that maydisplay a text or encoded message associated with the event. In one ormore embodiments, the LED visual indicator may flash slow yellow for amoderate type of concussion, and flash fast red for a severe type ofconcussion to give a quick overall view of the event without requiringany data communications. In addition, the LED may be asserted with anumber of flashes or other colors to indicate any temperature relatedevent or other event. One or more embodiments may also employ LCD 1402for example that may show text, or alternatively may display a codedmessage for sensitive health related information that a referee ormedical personnel may read or decode with an appropriate reader app on amobile device for example. In the lower right portion of the figure, theLCD display may produce an encoded message that states “PotentialConcussion 1500 degree/s/s rotational event detect—alert medicalpersonnel immediately”. Other paralysis diagnostic messages or any othertype of message that may be sensitive may be encoded and displayedlocally so that medical personnel may immediately begin assessing theuser/player/boxer without alarming other players with the diagnosticmessage for example, or without transmitting the message over the airwirelessly to avoid interception.

FIG. 15 illustrates an embodiment of templates characteristic of motionevents associated with different types of equipment and/or instrumentedclothing along with areas in which the motion capture sensor personalitymay change to more accurately or more efficiently capture dataassociated with a particular period of time and/or sub-event. As shown,the characteristic push off for a skateboard is shown in accelerationgraphs 1501 that display the X, Y and Z axes linear acceleration androtational acceleration values in the top 6 timelines, wherein timeincreases to the right. As shown, discrete positive x-axis accelerationcaptured is shown at 1502 and 1503 while the user pushes the skateboardwith each step, followed by negative acceleration as the skateboardslows between each push. In addition, y-axis wobbles during each pushare also captured while there is no change in the z axis linearacceleration and no rotational accelerations in this characteristictemplate or pattern of a skateboard push off or drive. Alternatively,the pattern may include a group of threshold accelerations in x atpredefined time windows with other thresholds or no threshold for wobblefor example that the captured data is compared against to determineautomatically the type of equipment that the motion capture element ismounted to or that the known piece of equipment is experiencingcurrently. This enables event based data saving and transmission forexample.

The pattern or template in graphs 1511 however show a running event asthe user slightly accelerates up and down during a running event. Sincethe user's speed is relatively constant there is relatively noacceleration in x and since the user is not turning, there is relativelyno acceleration in y (left/right). This pattern may be utilized tocompare within ranges for running for example wherein the patternincludes z axis accelerations in predefined time windows. Hence, the topthree graphs of graphs 1511 may be utilized as a pattern to notate arunning event at 1512 and 1513. The bottom three graphs may showcaptured data that are indicative of the user looking from side to sidewhen the motion capture element is mounted in a helmet and/or mouthpieceat 1514 and 1515, while captured data 1516 may be indicative of amoderate or sever concussion observed via a rotational motion of highenough angular degrees per second squared. In addition, the sensorpersonality may be altered dynamically at 1516 or at any other thresholdfor example to change the motion capture sensor rate of capture or bitsize of capture to more accurately in amplitude or time capture theevent. This enables dynamic alteration of quality of capture and/ordynamic change of power utilization for periods of interest, which isunknown in the art. In one or more embodiments, a temperature timelinemay also be recorded for embodiments of the invention that utilizetemperature sensors, either mounted within a helmet, mouthpiece or inany other piece of equipment or within the user's body for example.

FIG. 16 illustrates an embodiment of a protective mouthpiece 1601 infront view and at the bottom portion of the figure in top view, forexample as worn in any contact sport such as, but not limited to soccer,boxing, football, wrestling or any other sport for example. Embodimentsof the mouthpiece may be worn in addition to any other headgear with orwithout a motion capture element to increase the motion capture dataassociated with the user and correlate or in any other way combine orcompare the motion data and or events from any or all motion captureelements worn by the user. Embodiments of the mouthpiece and/or helmetshown in FIGS. 2A-B or in any other piece of equipment may also includea temperature sensor for example and as previously discussed.

FIG. 17 illustrates an embodiment of the algorithm utilized by anycomputer in FIG. 1 may display motion images and motion capture data ina combined format. In one or more embodiments, the motion capture dataand any event related start/stop times may be saved on the motioncapture element 111. One or more embodiments of the invention include amotion event recognition and video synchronization system that includesat least one motion capture element that may couple with a user or pieceof equipment or mobile device coupled with the user. The at least onemotion capture element may include a memory, a sensor that may captureany combination of values associated with an orientation, position,velocity, acceleration, angular velocity, and angular acceleration ofthe at least one motion capture element, a communication interface, amicrocontroller coupled with the memory, the sensor and thecommunication interface. The microcontroller may collect data thatincludes sensor values from the sensor, store the data in the memory,analyze the data and recognize an event within the data to determineevent data, transmit the event data associated with the event via thecommunication interface. The system may also include a mobile devicethat includes a computer, a communication interface that may communicatewith the communication interface of the motion capture element to obtainthe event data associated with the event, wherein the computer iscoupled with the communication interface, wherein the computer mayreceive the event data from the computer's communication interface. Thecomputer may also analyze the event data to form motion analysis data,store the event data, or the motion analysis data, or both the eventdata and the motion analysis data, obtain an event start time and anevent stop time from the event. In one or more embodiments, the computermay request image data from camera that includes a video captured atleast during a timespan from the event start time to the event stop timeand display an event video on a display that includes both the eventdata, the motion analysis data or any combination thereof that occursduring the timespan from the event start time to the event stop time andthe video captured during the timespan from the event start time to theevent stop time.

FIG. 17 illustrates an embodiment of the algorithm utilized by anycomputer in FIG. 1 may display motion images and motion capture data ina combined format. In one or more embodiments, the motion capture dataand any event related start/stop times may be saved on the motioncapture element 111. One or more embodiments of the invention include amotion event recognition and video synchronization system that includesat least one motion capture element that may couple with a user or pieceof equipment or mobile device coupled with the user. The at least onemotion capture element may include a memory, a sensor that may captureany combination of values associated with an orientation, position,velocity, acceleration, angular velocity, and angular acceleration ofthe at least one motion capture element, a communication interface, amicrocontroller coupled with the memory, the sensor and thecommunication interface. The microcontroller may collect data thatincludes sensor values from the sensor, store the data in the memory,analyze the data and recognize an event within the data to determineevent data, transmit the event data associated with the event via thecommunication interface. The system may also include a mobile devicethat includes a computer, a communication interface that may communicatewith the communication interface of the motion capture element to obtainthe event data associated with the event, wherein the computer iscoupled with the communication interface, wherein the computer mayreceive the event data from the computer's communication interface. Thecomputer may also analyze the event data to form motion analysis data,store the event data, or the motion analysis data, or both the eventdata and the motion analysis data, obtain an event start time and anevent stop time from the event. In one or more embodiments, the computermay request image data from camera that includes a video captured atleast during a timespan from the event start time to the event stop timeand display an event video on a display that includes both the eventdata, the motion analysis data or any combination thereof that occursduring the timespan from the event start time to the event stop time andthe video captured during the timespan from the event start time to theevent stop time.

In one or more embodiments, the computer may synchronize based on thefirst time associated with the data or the event data obtained from theat least one motion capture element coupled with the user or the pieceof equipment or the mobile device coupled with the user, and at leastone time associated with the at least one video to create at least onesynchronized event video. In at least one embodiment, the computer maystore the at least one synchronized event video in the computer memorywithout at least a portion of the at least one video outside of theevent start time to the event stop time. According to at least oneembodiment, the computer may display a synchronized event videoincluding both of the event data, motion analysis data or anycombination thereof that occurs during a timespan from the event starttime to the event stop time, and the video captured during the timespanfrom the event start time to the event stop time.

In one or more embodiments, the computer may transmit the at least onesynchronized event video or a portion of the at least one synchronizedevent video to one or more of a repository, a viewer, a server, anothercomputer, a social media site, a mobile device, a network, and anemergency service.

When a communication channel is available, motion capture data and anyevent related start/stop times are pushed to, or obtained by orotherwise received by any computer, e.g., 101, 102, 102 a, 102 b, 105 at1701. The clock difference between the clock on the sensor and/or inmotion capture data times may also be obtained. This may be performed byreading a current time stamp in the incoming messages and comparing theincoming message time with the current time of the clock of the localcomputer, see also FIG. 18 for example for more detail onsynchronization. The difference in clocks from the sensor and computermay be utilized to request images data from any camera local or pointingat the location of the event for the adjusted times to take into accountany clock difference at 1702. For example, the computer may requestimages taken at the time/location by querying all cameras 103, 104, oron devices 101, 102 and/or 102 a for any or all such devices havingimages taken nearby, e.g., based on GPS location or wireless range,and/or pointed at the event obtained from motion capture element 111. Ifa device is not nearby, but is pointing at the location of the event, asdetermined by its location and orientation when equipped with amagnetometer for example, then it may respond as well with images forthe time range. Any type of camera that may communicate electronicallymay be queried, including nanny cameras, etc. For example, a message maybe sent by mobile computer 101 after receiving events from motioncapture sensor 111 wherein the message may be sent to any cameras forexample within wireless range of mobile device 101. Alternatively, or incombination, mobile device 101 may send a broadcast message asking forany cameras identities that are within a predefined distance from thelocation of the event or query for any cameras pointed in the directionof the event even if not relatively close. Upon receiving the list ofpotential cameras, mobile device 101 may query them for any imagesobtained in a predefined window around the event for example. Thecomputer may receive image data or look up the images locally if thecomputer is coupled with a camera at 1703. In one or more embodiments,the server 172 may iterate through videos and events to determine anythat correlate and automatically trim the videos to correspond to thedurations of the event start and stop times. Although wirelesscommunications may be utilized, any other form of transfer of image datais in keeping with the spirit of the invention. The data from the eventwhether in numerical or graphical overlay format or any other formatincluding text may be shown with or otherwise overlaid onto thecorresponding image for that time at 1704. This is shown graphically attime 1710, i.e., the current time, which may be scrollable for example,for image 1711 showing a frame of a motion event with overlaid motioncapture data 1712. See FIG. 6 for combined or simultaneouslynon-overlaid data for example.

FIG. 18 illustrates an embodiment of the synchronization architecturethat may be utilized by one or more embodiments of the invention.Embodiments may synchronize clocks in the system using any type ofsynchronization methodology and in one or more embodiments the computer160 on the mobile device 101 may determine a clock difference betweenthe motion capture element 111 and the mobile device and synchronize themotion analysis data with the video. For example, one or moreembodiments of the invention provides procedures for multiple recordingdevices to synchronize information about the time, location, ororientation of each device, so that data recorded about events fromdifferent devices can be combined. Such recording devices may beembedded sensors, mobile phones with cameras or microphones, or moregenerally any devices that can record data relevant to an activity ofinterest. In one or more embodiments, this synchronization isaccomplished by exchanging information between devices so that thedevices can agree on a common measurement for time, location, ororientation. For example, a mobile phone and an embedded sensor mayexchange messages across link 1802, e.g., wirelessly, with the currenttimestamps of their internal clocks; these messages allow a negotiationto occur wherein the two devices agree on a common time. Such messagesmay be exchanged periodically as needed to account for clock drift ormotion of the devices after a previous synchronization. In otherembodiments, multiple recording devices may use a common server or setof servers 1801 to obtain standardized measures of time, location, ororientation. For example, devices may use a GPS system to obtainabsolute location information for each device. GPS systems may also beused to obtain standardized time. NTP (Network Time Protocol) serversmay also be used as standardized time servers. Using servers allowsdevices to agree on common measurements without necessarily beingconfigured at all times to communicate with one another.

FIG. 19 illustrates the detection of an event by one of the motioncapture sensors 111, transmission of the event detection, here shown asarrows emanating from the centrally located sensor 111 in the figure, toother motion capture sensors 111 and/or cameras, e.g., on mobile device101, saving of the event motion data and trimming of the video tocorrespond to the event. In one or more embodiments of the invention,some of the recording devices may detect the occurrence of variousevents of interest. Some such events may occur at specific moments intime; others may occur over a time interval, wherein the detectionincludes detection of the start of an event and of the end of an event.These devices may record any combination of the time, location, ororientation of the recording device, for example included in memorybuffer 4610 for example along with the event data, or in any other datastructure, using the synchronized measurement bases for time, location,and orientation described above.

Embodiments of the computer on the mobile device may discard at least aportion of the video outside of the event start time to the event stop,for example portions 1910 and 1911 before and after the event or eventwith predefined pre and post intervals 1902 and 1903. In one or moreembodiments, the computer may command or instruct other devices,including the computer or other computers, or another camera, or thecamera or cameras that captured the video, to discard at least a portionof the video outside of the event start time to the event stop time. Forexample, in one or more embodiments of the invention, some of therecording devices capture data continuously to memory while awaiting thedetection of an event. To conserve memory, some devices may store datato a more permanent local storage medium, or to server 172, only whenthis data is proximate in time to a detected event. For example, in theabsence of an event detection, newly recorded data may ultimatelyoverwrite previously recorded data in memory, depending on the amount ofmemory in each device that is recording motion data or video data. Acircular buffer may be used in some embodiments as a typicalimplementation of such an overwriting scheme. When an event detectionoccurs, the recording device may store some configured amount of dataprior to the start of the event, near start of pre interval 1902 andsome configured amount of data after the end of the event, near 1903, inaddition to storing the data captured during the event itself, namely1901. Any pre or post time interval is considered part of the eventstart time and event stop time so that context of the event is shown inthe video for example. This gives context to the event, for example theamount of pre time interval may be set per sport for example to enable asetup for a golf swing to be part of the event video even though itoccurs before the actual event of striking the golf ball. The followthrough may be recorded as per the amount of interval allotted for thepost interval as well.

Embodiments of the system may include a server computer remote to themobile device and wherein the server computer may discard at least aportion of the video outside of the event start time to the event stopand return the video captured during the timespan from the event starttime to the event stop time to the computer in the mobile device. Theserver or mobile device may combine or overlay the motion analysis dataor event data, for example velocity or raw acceleration data with oronto the video to form event video 1900, which may thus greatly reducethe amount of video storage required as portions 1910 and 1911 may be ofmuch larger length in time that the event in general.

Embodiments of the at least one motion capture element, for example themicroprocessor, may transmit the event to at least one other motioncapture sensor or at least one other mobile device or any combinationthereof, and wherein the at least one other motion capture sensor or theat least one other mobile device or any combination thereof may savedata, or transmit data, or both associated with the event, even if theat least one other motion capture element has not detected the event.For example, in embodiments with multiple recording devices operatingsimultaneously, one such device may detect an event and send a messageto other recording devices that such an event detection has occurred.This message can include the timestamp of the start and/or stop of theevent, using the synchronized time basis for the clocks of the variousdevices. The receiving devices, e.g., other motion capture sensorsand/or cameras may use the event detection message to store dataassociated with the event to nonvolatile storage, for example withinmotion capture element 111 or mobile device 101 or server 172. Thedevices may store some amount of data prior to the start of the eventand some amount of data after the end of the event, 1902 and 1903respectively, in addition to the data directly associated with the event1901. In this way all devices can record data simultaneously, but use anevent trigger from only one of the devices to initiate saving ofdistributed event data from multiple sources.

Embodiments of the computer may save the video from the event start timeto the event stop time with the motion analysis data that occurs fromthe event start time to the event stop time or a remote server may beutilized to save the video. In one or more embodiments of the invention,some of the recording devices may not be in direct communication witheach other throughout the time period in which events may occur. Inthese situations, devices may save complete records of all of the datathey have recorded to permanent storage or to a server. Saving of onlydata associated with events may not be possible in these situationsbecause some devices may not be able to receive event trigger messages.In these situations, saved data can be processed after the fact toextract only the relevant portions associated with one or more detectedevents. For example, multiple mobile devices may record video of aplayer or performer, and upload this video continuously to server 172for storage. Separately the player or performer may be equipped with anembedded sensor that is able to detect events such as particular motionsor actions. Embedded sensor data may be uploaded to the same servereither continuously or at a later time. Since all data, including thevideo streams as well as the embedded sensor data, is generallytimestamped, video associated with the events detected by the embeddedsensor can be extracted and combined on the server. Embodiments of theserver or computer may, while a communication link is open between theat least one motion capture sensor and the mobile device, discard atleast a portion of the video outside of the event start time to theevent stop and save the video from the event start time to the eventstop time with the motion analysis data that occurs from the event starttime to the event stop time. Alternatively, if the communication link isnot open, embodiments of the computer may save video and after the eventis received after the communication link is open, then discard at leasta portion of the video outside of the event start time to the event stopand save the video from the event start time to the event stop time withthe motion analysis data that occurs from the event start time to theevent stop time. For example, in some embodiments of the invention, datamay be uploaded to a server as described above, and the location andorientation data associated with each device's data stream may be usedto extract data that is relevant to a detected event. For example, alarge set of mobile devices may be used to record video at variouslocations throughout a golf tournament. This video data may be uploadedto a server either continuously or after the tournament. After thetournament, sensor data with event detections may also be uploaded tothe same server. Post-processing of these various data streams canidentify particular video streams that were recorded in the physicalproximity of events that occurred and at the same time. Additionalfilters may select video streams where a camera was pointing in thecorrect direction to observe an event. These selected streams may becombined with the sensor data to form an aggregate data stream withmultiple video angles showing an event.

The system may obtain video from a camera coupled with the mobiledevice, or any camera that is separate from or otherwise remote from themobile device. In one or more embodiments, the video is obtained from aserver remote to the mobile device, for example obtained after a queryfor video at a location and time interval.

Embodiments of the server or computer may synchronize the video and theevent data, or the motion analysis data via image analysis to moreaccurately determine a start event frame or stop event frame in thevideo or both, that is most closely associated with the event start timeor the event stop time or both. In one or more embodiments of theinvention, synchronization of clocks between recording devices may beapproximate. It may be desirable to improve the accuracy ofsynchronizing data feeds from multiple recording devices based on theview of an event from each device. In one or more embodiments,processing of multiple data streams is used to observe signatures ofevents in the different streams to assist with fine-grainedsynchronization. For example, an embedded sensor may be synchronizedwith a mobile device including a video camera, but the timesynchronization may be accurate only to within 100 milliseconds. If thevideo camera is recording video at 30 frames per second, the video framecorresponding to an event detection on the embedded sensor can only bedetermined within 3 frames based on the synchronized timestamps alone.In one embodiment of the device, video frame image processing can beused to determine the precise frame corresponding most closely to thedetected event. See FIG. 8 and description thereof for more detail. Forinstance, a shock from a snowboard hitting the ground as shown in FIG.17, that is detected by an inertial sensor may be correlated with theframe at which the geometric boundary of the snowboard makes contactwith the ground. Other embodiments may use other image processingtechniques or other methods of detecting event signatures to improvesynchronization of multiple data feeds.

Embodiments of the at least one motion capture element may include alocation determination element that may determine a location that iscoupled with the microcontroller and wherein the microcontroller maytransmit the location to the computer on the mobile device. In one ormore embodiments, the system further includes a server wherein themicrocontroller may transmit the location to the server, either directlyor via the mobile device, and wherein the computer or server may formthe event video from portions of the video based on the location and theevent start time and the event stop time. For example, in one or moreembodiments, the event video may be trimmed to a particular length ofthe event, and transcoded to any or video quality for example on mobiledevice 101 or on server 172 or on computer 105 or any other computercoupled with the system, and overlaid or otherwise integrated withmotion analysis data or event data, e.g., velocity or acceleration datain any manner. Video may be stored locally in any resolution, depth, orimage quality or compression type to store video or any other techniqueto maximize storage capacity or frame rate or with any compression typeto minimize storage, whether a communication link is open or not betweenthe mobile device, at least one motion capture sensor and/or server. Inone or more embodiments, the velocity or other motion analysis data maybe overlaid or otherwise combined, e.g., on a portion beneath the video,that includes the event start and stop time, that may include any numberof seconds before and/or after the actual event to provide video of theswing before a ball strike event for example. In one or moreembodiments, the at least one motion capture sensor and/or mobiledevice(s) may transmit events and video to a server wherein the servermay determine that particular videos and sensor data occurred in aparticular location at a particular time and construct event videos fromseveral videos and several sensor events. The sensor events may be fromone sensor or multiple sensors coupled with a user and/or piece ofequipment for example. Thus the system may construct short videos thatcorrespond to the events, which greatly decreases video storagerequirements for example.

In one or more embodiments, the microcontroller or the computer maydetermine a location of the event or the microcontroller and thecomputer may determine the location of the event and correlate thelocation, for example by correlating or averaging the location toprovide a central point of the event, and/or erroneous location datafrom initializing GPS sensors may be minimized. In this manner, a groupof users with mobile devices may generate videos of a golfer teeing off,wherein the event location of the at least one motion capture device maybe utilized and wherein the server may obtain videos from the spectatorsand generate an event video of the swing and ball strike of theprofessional golfer, wherein the event video may utilize frames fromdifferent cameras to generate a BULLET TIME® video from around thegolfer as the golfer swings. The resulting video or videos may betrimmed to the duration of the event, e.g., from the event start time tothe event stop time and/or with any pre or post predetermined timevalues around the event to ensure that the entire event is capturedincluding any setup time and any follow through time for the swing orother event.

In at least one embodiment, the computer may request or broadcast arequest from camera locations proximal to the event or oriented to viewthe event, or both, and may request the video from the at least onecamera proximal to the event, wherein the video includes the event. Forexample, in one or more embodiments, the computer on the mobile devicemay request at least one image or video that contains the event from atleast one camera proximal to the event directly by broadcasting arequest for any videos taken in the area by any cameras, optionally thatmay include orientation information related to whether the camera wasnot only located proximally to the event, but also oriented or otherwisepointing at the event. In other embodiments, the video may be requestedby the computer on the mobile device from a remote server. In thisscenario, any location and/or time associated with an event may beutilized to return images and/or video near the event or taken at a timenear the event, or both. In one or more embodiments, the computer orserver may trim the video to correspond to the event duration and again,may utilize image processing techniques to further synchronize portionsof an event, such as a ball strike with the corresponding frame in thevideo that matches the acceleration data corresponding to the ballstrike on a piece of equipment for example.

Embodiments of the computer on the mobile device or on the server maydisplay a list of one or more times at which an event has occurred orwherein one or more events has occurred. In this manner, a user may findevents from a list to access the event videos in rapid fashion.

Embodiments of the invention may include at least one motion capturesensor that is physically coupled with the mobile device. Theseembodiments enable any type of mobile phone or camera system with anintegrated sensor, such as any type of helmet mounted camera or anymount that includes both a camera and a motion capture sensor togenerate event data and video data.

In one or more embodiments of the invention, the system enablesintegration of motion event data and video event data. FIG. 1illustrates core elements of embodiments of such a system. Motion eventdata may be provided by one or more motion capture elements 111, whichmay be attached to user 150 at location L1, to a piece of equipment 110,or to a mobile device 130. These motion capture elements may include oneor more sensors that measure motion values such as orientation,position, velocity, acceleration, angular velocity, and angularacceleration. The motion capture elements may also include a memory, forstoring capture data, and a microprocessor for analyzing this data. Theymay also include a communication interface for communicating with otherdevices and for transferring motion capture data. The communicationinterface may be wired or wireless. It may include for example, withoutlimitation: a radio for a wireless network such as for exampleBluetooth, Bluetooth Low Energy, 802.11, or cellular networks; a networkinterface card for a LAN or WAN wired network using a protocol such asfor example Ethernet; a serial interface such as for example RS232 orUSB; or a local bus interface such as for example ISA, PCI, or SPI.

In some embodiments, the microprocessor coupled with the motion captureelement may collect data from the sensor, store the data in its memory,and possibly analyze the data to recognize an event within the data. Itmay then transmit the raw motion data or the event data via the attachedwired or wireless communication interface. This raw motion data or eventdata may include other information such an identifier of the motioncapture element, the user, or the equipment, and an identifier of thetype of event detected by the motion capture element.

In some embodiments, the system may also include one or more computers105 (a laptop or desktop computer), 160 (a mobile phone CPU), or othercomputers in communication with sensors or cameras. FIG. 1A illustratespossible components of an embodiment of a computer processor or“computer” 160 integrated into a mobile device. Computers may have acommunication interface 164 that can communicate with the communicationinterfaces of one or more motion capture elements 111 to receive theevent data associated with motion events. Computers may also have wiredcommunication interfaces to communicate with motion capture elements orwith other components or other computers. One or more embodiments mayuse combinations of wired and wireless communication interfaces. Thecomputer may receive raw motion data, and it may analyze this data todetermine events. In other embodiments, the determination of events mayoccur in the motion capture element 111, and the computer (such as 105or 160) may receive event data. Combinations of these two approaches arealso possible in some embodiments.

In some embodiments, the computer or computers may further analyze eventdata to generate motion analysis data. This motion analysis data mayinclude characteristics of interest for the motion recorded by themotion capture element or elements. One or more computers may store themotion data, the event data, the motion analysis data, or combinationsthereof for future retrieval and analysis. Data may be stored locally,such as in memory 162, or remotely as in database 172. In someembodiments the computer or computers may determine the start time andend time of a motion event from the event data. They may then requestimage data from a camera, such as 103, 130, 130 a, or 130 b, that hascaptured video or one or more images for some time interval at leastwithin some portion of the time between this event start time and eventend time. The term video in this specification will include individualimages as well as continuous video, including the case of a camera thattakes a single snapshot image during an event interval. This video datamay then be associated with the motion data to form a portion of a videoand motion capture integration system. As shown camera 103 at locationL2 has field of view F2, while camera on mobile device 102 a at positionL3 has field of view F3. For cameras, whose field of view overlaps anevent, intelligent selection of the best video is achieved in at leastone embodiment via image analysis. Sensors 107, such as environmentalsensors may also be utilized to trigger events or at least be queriedfor values to combine with event videos, for example wind speed,humidity, temperature, sound, etc. In other embodiments, the system mayquery for video and events within a predefined area around location L1,and may also use field of view of each camera at L2 and L3 to determineif the video has potentially captured the event.

In some embodiments, the request of video from a camera may occurconcurrently with the capture or analysis of motion data. In suchembodiments, the system will obtain or generate a notification that anevent has begun, and it will then request that video be streamed fromone or more cameras to the computer until the end of the event isdetected. In other embodiments, the user may gesture by tapping ormoving a motion capture sensor a predefined number of time to signifythe start of an event, for example tapping a baseball bat twice againstthe batter's shoes may signify the start of an at bat event.

In other embodiments, the request of video may occur after a camera(such as 103) has uploaded its video records to another computer, suchas a server 172. In this case, the computer will request video from theserver 172 rather than directly from the camera.

In some embodiments, the computer or computers may perform asynchronization of the motion data and the video data. Varioustechniques may be used to perform this synchronization. FIG. 1Eillustrates an embodiment of this synchronization process. Motioncapture element 111 includes a clock 12901, designated as “Clock S”.When an event occurs, the motion capture element generates timestampeddata 12910, with times t_(1S), t_(2S), t_(3S), etc. from Clock S. Camera103 captures video or images of some portion of the event. The cameraalso includes a clock 12902, designated as “Clock I”. The cameragenerates timestamped image data 12911, with times t_(1I), t_(2I),t_(3I), etc. from Clock I. Computer 105 receives the motion data and theimage data. The computer contains another clock 12903, designated as“Clock C”. The computer executes a synchronization process that consistsof aligning the various time scales from the three clocks 12912, 12913,and 12914. The result of this synchronization is a correspondencebetween the clocks 12915. In general the alignment of clocks may requiregenerating clock differences as well as stretching or shrinkingtimescales to reflect different clock rates. In some embodiments,individual data frames or image frames may not be timestamped, butinstead the first or last frame may be associated with a time and theremay be a known clock rate for frame capture. In other embodiments datamay not include a timestamp, but may be transmitted immediately uponcapture so that the computer can estimate the time of capture based ontime of receipt and possible network latency.

In the embodiment illustrated in FIG. 1E, the computer generates asynchronized event video 12920, which will include at least some of themotion data, event data, or motion analysis data obtained or calculatedbetween the event start time and the event end time, and some of thevideo or images obtained from the camera within this start time and endtime. This synchronized event video provides an augmented, integratedrecord of the event that incorporates both motion data and image data.In the example shown the synchronization process has assigned the firstimage frame F₁ to time t_(5C), and the first motion data frame D₁ totime t_(6C). In this example, the image frame capture rate is twice thedata frame capture rate.

One or more embodiments of the invention may also obtain at least onevideo start time and at least one video stop time associated with atleast one video from at least one camera. One of the computers on thesystem may optionally synchronize the event data, the motion analysisdata or any combination thereof with the at least one video based on afirst time associated with the data or the event data obtained from theat least one motion capture element coupled with the user or the pieceof equipment or the mobile device coupled with the user and at least onetime associated the at least one video to create at least onesynchronized event video. Embodiments command at least one camera totransfer the at least one synchronized event video captured at leastduring a timespan from within the event start time to the event stoptime to another computer without transferring at least a portion of thevideo that occurs outside of the at least one video that occurs outsideof the timespan from within the event start time to the event stop timeto the another computer. One or more embodiments also may overlay asynchronized event video including both of the event data, the motionanalysis data or any combination thereof that occurs during the timespanfrom the event start time to the event stop time and the video capturedduring the timespan from the event start time to the event stop time.

In one or more embodiments of the invention, a computer may discardvideo that is outside of the time interval of an event, measured fromthe start time of an even to the stop time of an event. This discardingmay save considerable storage resources for video storage by saving onlythe video associated with an event of interest. FIG. 19 illustrates anembodiment of this process. Synchronized event video 1900 includesmotion and image data during an event, 1901, and for some predefined preand post intervals 1902 and 1903. Portions 1910 and 1911 before andafter the pre and post intervals are discarded.

In one or more embodiments, a computer that may receive or processmotion data or video data may be a mobile device, including but notlimited to a mobile telephone, a smartphone 120, a tablet, a PDA, alaptop 105, a notebook, or any other device that can be easilytransported or relocated. In other embodiments, such a computer may beintegrated into a camera 103, 104, and in particular it may beintegrated into the camera from which video data is obtained. In otherembodiments, such a computer may be a desktop computer or a servercomputer 152, including but not limited to virtual computers running asvirtual machines in a data center or in a cloud-based service. In someembodiments, the system may include multiple computers of any of theabove types, and these computers may jointly perform the operationsdescribed in this specification. As will be obvious to one skilled inthe art, such a distributed network of computers can divide tasks inmany possible ways and can coordinate their actions to replicate theactions of a single centralized computer if desired. The term computerin this specification is intended to mean any or all of the above typesof computers, and to include networks of multiple such computers actingtogether.

In one or more embodiments, a microcontroller associated with a motioncapture element 111, and a computer 105, may obtain clock informationfrom a common clock and to set their internal local clocks 12901 and12903 to this common value. This methodology may be used as well to setthe internal clock of a camera 12902 to the same common clock value. Thecommon clock value may be part of the system, or it may be an externalclock used as a remote time server. Various techniques may be used tosynchronize the clocks of individual devices to the common clock,including Network Time Protocol or other similar protocols. FIG. 18illustrates an embodiment of the invention that uses an NTP or GPSserver 1801 as a common time source. By periodically synchronizingclocks of the devices to a common clock 1801, motion capture data andvideo data can be synchronized simply by timestamping them with the timethey are recorded.

In one or more embodiments, the computer may obtain or create a sequenceof synchronized event videos. The computer may display a compositesummary of this sequence for a user to review the history of the events.FIG. 20 illustrates an embodiment of this process. Video clips 1900 a,1900 b, 1900 c, 1900 d, and 1900 e are obtained at different timescorresponding to different events. Video or motion data prior to theseevents, 1910 and 1911, and between these events, 1910 a, 1901 b, 1910 c,and 1910 d, is removed. The result is composite summary 2000. In someembodiments, this summary may include one or more thumbnail imagesgenerated from the videos. In other embodiments, the summary may includesmaller selections from the full event video. The composite summary mayalso include display of motion analysis or event data associated witheach synchronized event video. In some embodiments, the computer mayobtain or accept a metric, such as a metric associated with the at leastone synchronized event video, and display the value of this metric foreach event. The display of these metric values may vary in differentembodiments. In some embodiments, the display of metric values may be abar graph, line graph, or other graphical technique to show absolute orrelative values. In other embodiments color-coding or other visualeffects may be used. In other embodiments, the numerical values of themetrics may be shown. Some embodiments may use combinations of theseapproaches. In the example illustrated in FIG. 20 the metric value forSpeed associated with each event is shown as a graph with circles foreach value.

In one or more embodiments, the computer may accept selection criteriafor a metric 2010 of interest associated with the motion analysis dataor event data of the sequence of events. For example, a user may providecriteria such as metrics 2010 exceeding a threshold, or inside a range,or outside a range, as 2011. Any criteria may be used that may beapplied to the metric values 2010, 2011 of the events. In response tothe selection criteria, the computer may display only the synchronizedevent videos or their summaries (such as thumbnails) that meet theselection criteria. FIG. 20 illustrates an embodiment of this process. Aselection criterion 2010 has been provided specifying that Speed 2020should be at least 5 at 2021. The computer responds by displaying 2001with Clips 1 through Clip 4; Clip 5 has been excluded based on itsassociated speed.

In one or more embodiments, the computer may determine a matching set ofsynchronized event videos that have values associated with the metricthat pass the selection criteria, and display the matching set ofsynchronized event videos or corresponding thumbnails thereof along withthe value associated with the metric for each of the matching set ofsynchronized event videos or the corresponding thumbnails.

In some embodiments of the invention, the computer may sort and ranksynchronized event videos for display based on the value of a selectedmetric. This sorting and ranking may occur in some embodiments inaddition to the filtering based on selection criteria as describedabove. The computer may display an ordered list of metric values, alongwith videos or thumbnails associated with the events. Continuing theexample above as illustrated in FIG. 20, if a sorted display based onSpeed is specified, the computer generates 2002 with clips reorderedfrom highest speed to lowest speed. In one or more embodiments, thecomputer may generate a highlight reel, or fail reel, or both, forexample of the matching set of synchronized events, that combines thevideo for events that satisfy selection criteria. Such a highlight reelor fail reel, in at least one embodiment, may include the entire videofor the selected events, or a portion of the video that corresponds tothe important moments in the event as determined by the motion analysis.In some embodiments, the highlight reel or fail reel may includeoverlays of data or graphics on the video or on selected frames showingthe value of metrics from the motion analysis. Such a highlight reel orfail reel may be generated automatically for a user once the userindicates which events to include by specifying selection criteria. Insome embodiments, the computer may allow the user to edit the highlightreel or fail reel to add or remove events, to lengthen or shorten thevideo shown for each event, to add or remove graphic overlays for motiondata, or to add special effects or soundtracks.

In one or more embodiments, a video and motion integration system mayincorporate multiple cameras, such as cameras 103, 104, 130, 130 a, and130 b. In such embodiments, a computer may request video correspondingto an event timeframe from multiple cameras that captured video duringthis timeframe. Each of these videos may be synchronized with the eventdata and the motion analysis data as described above for thesynchronization of a single video. Videos from multiple cameras mayprovide different angles or views of an event, all synchronized tomotion data and to a common time base.

In one or more embodiments with multiple cameras, the computer mayselect a particular video from the set of possible videos associatedwith an event. The selected video may be the best or most complete viewof the event based on various possible criteria. In some embodiments,the computer may use image analysis of each of the videos to determinethe best selection. For example, some embodiments may use image analysisto determine which video is most complete in that the equipment orpeople of interest are least occluded or are most clearly visible. Insome embodiments, this image analysis may include analysis of the degreeof shaking of a camera during the capture of the video, and selection ofthe video with the most stable images. FIG. 21 illustrates an embodimentof this process. Motion capture element 111 indicates an event, which isrecorded by cameras 103 a and 103 b. Computer 105 retrieves video fromboth cameras. Camera 103 b has shaking 2101 during the event. Todetermine the video with least shaking, Computer 105 calculates aninter-frame difference for each video. For example, this difference mayinclude the sum of the absolute value of differences in each pixel's RGBvalues across all pixels. This calculation results in frame differences2111 for camera 103 b and 2110 for camera 103 a. The inter-framedifferences in both videos increase as the event occurs, but they areconsistently higher in 2111 because of the increased shaking. Thecomputer is thus able to automatically select video 2110 in process2120. In some embodiments, a user 2130 may make the selection of apreferred video, or the user may assist the computer in making theselection by specifying the most important criteria.

In one or more embodiments of the invention, the computer may obtain orgenerate notification of the start of an event, and it may then monitorevent data and motion analysis data from that point until the end of theevent. For example, the microcontroller associated with the motioncapture element may send event data periodically to the computer oncethe start of an event occurs; the computer can use this data to monitorthe event as it occurs. In some embodiments, this monitoring data may beused to send control messages to a camera that can record video for theevent. In embodiments with multiple cameras, control messages could bebroadcast or could be send to a set of cameras during the event. In atleast one embodiment, the computer may send a control message local tothe computer or external to the computer to at least one camera.

In some embodiments, these control messages sent to the camera orcameras may modify the video recording parameters of the at least onevideo based on the data associated with the event, including the motionanalysis data. FIG. 22 illustrates an embodiment of this process. Motioncapture sensor 111 transmits motion data to computer 105, which thensends control messages to camera 103. In the example shown, equipment110 is initially at rest prior to an event. The computer detects thatthere is no active event, and sends message 2210 to the camerainstructing it to turn off recording and await events. Motion 2201begins and the computer detects the start of the event; it sends message2211 to the camera to turn on recording, and the camera begins recordingvideo frames 2321 at a normal rate. Motion increases rapidly at 2202 andthe computer detects high speed; it sends message 2212 to the camera toincrease its frame rate to capture the high speed event. The cameragenerates video frames 2322 at a high rate. By using a higher frame rateduring rapid motion, the user can slow the motion down during playbackto observe high motion events in great detail. At 2203 the eventcompletes, and the computer sends message 2213 to the camera to stoprecording. This conserves camera power as well as video memory betweenevents.

More generally in some embodiments a computer may send control messagesto a camera or cameras to modify any relevant video recording parametersin response to event data or motion analysis data. These recordingparameters may for example include the frame rate, resolution, colordepth, color or grayscale, compression method, and compression qualityof the video, as well as turning recording on or off.

In one or more embodiments of the invention, the computer may accept asound track, for example from a user, and integrate this sound trackinto the synchronized event video. This integration would for exampleadd an audio sound track during playback of an event video or ahighlight reel or fail reel. Some embodiments may use event data ormotion analysis data to integrate the sound track intelligently into thesynchronized event video. For example, some embodiments may analyze asound track to determine the beats of the sound track based for instanceon time points of high audio amplitude. The beats of the sound track maythen be synchronized with the event using event data or motion analysisdata. For example, such techniques may automatically speed up or slowdown a sound track as the motion of a user or object increases ordecreases. These techniques provide a rich media experience with audioand visual cues associated with an event.

In one or more embodiments, a computer may playback a synchronized eventvideo on one or more displays. These displays may be directly attachedto the computer, or may be remote on other devices. Using the event dataor the motion analysis data, the computer may modify the playback to addor change various effects. These modifications may occur multiple timesduring playback, or even continuously during playback as the event datachanges.

As an example, in some embodiments the computer may modify the playbackspeed of a synchronized event video based on the event data or themotion analysis data. For instance, during periods of low motion theplayback may occur at normal speed, while during periods of high motionthe playback may switch to slow motion to highlight the details of themotion. Modifications to playback speed may be made based on anyobserved or calculated characteristics of the event or the motion. Forinstance, event data may identify particular sub-events of interest,such as the striking of a ball, beginning or end of a jump, or any otherinteresting moments. The computer may modify the playback speed to slowdown playback as the synchronized event video approaches thesesub-events. This slowdown could increase continuously to highlight thesub-event in fine detail. Playback could even be stopped at thesub-event and await input from the user to continue. Playback slowdowncould also be based on the value of one or more metrics from the motionanalysis data or the event data. For example, motion analysis data mayindicate the speed of a moving baseball bat or golf club, and playbackspeed could be adjusted continuously to be slower as the speed of suchan object increases. Playback speed could be made very slow near thepeak value of such metrics.

FIG. 23 illustrates an embodiment of variable speed playback usingmotion data. Motion capture element 111 records motion sensorinformation including linear acceleration on the x-axis 1501. (Ingeneral, many additional sensor values may be recorded as well; thisexample uses a single axis for simplicity.) Event threshold 2301 definesevents of interest when the x-axis linear acceleration exceeds thisthreshold. Events are detected at 1502 and 1503. Event 1502 begins at2302 and completes at 2303. On playback, normal playback speed 2310 isused between events. As the beginning of event 1502 approaches, playbackspeed is reduced starting at 2311 so the user can observe pre-eventmotion in greater detail. During the event playback speed is very slowat 2313. After the event end at 2303 playback speed increases graduallyback to normal speed at 2312.

In other embodiments, modifications could be made to other playbackcharacteristics not limited to playback speed. For example, the computercould modify any or all of playback speed, image brightness, imagecolors, image focus, image resolution, flashing special effects, or useof graphic overlays or borders. These modifications could be made basedon motion analysis data, event data, sub-events, or any othercharacteristic of the synchronized event video. As an example, asplayback approaches a sub-event of interest, a flashing special effectcould be added, and a border could be added around objects of interestin the video such as a ball that is about to be struck by a piece ofequipment.

In embodiments that include a sound track, modifications to playbackcharacteristics can include modifications to the playbackcharacteristics of the sound track. For example, such modifications mayinclude modifications to the volume, tempo, tone, or audio specialeffects of the sound track. For instance, the volume and tempo of asound track may be increased as playback approaches a sub-event ofinterest, to highlight the sub-event and to provide a more dynamicexperience for the user watching and listening to the playback.

In one or more embodiments of the invention, a computer may use eventdata or motion analysis data to selectively save only portions of videostream or recorded video. This is illustrated in FIG. 19 where videoportions 1910 and 1911 are discarded to save only the event video 1901with a pre-event portion 1902 and a post-event portion 1903. Suchtechniques can dramatically reduce the requirements for video storage byfocusing on events of interest. In some embodiments, a computer may havean open communication link to a motion capture sensor while an event isin progress. The computer may then receive or generate a notification ofa start of an event, and begin saving video at that time; it may thencontinue saving video until it receives or generates a notification ofthe end of the event. The computer may also send control messages to acamera or cameras during the event to initiate and terminate saving ofvideo on the cameras, as illustrated in FIG. 22.

In other embodiments, the computer may save or receive videos and eventdata after the event has completed, rather than via a live communicationlink open through the event. In these embodiments, the computer cantruncate the saved video to discard a portion of the video outside theevent of interest. For example, a server computer 152 may be used as arepository for both video and event data. The server could correlate theevent data and the video after upload, and truncate the saved video toonly the timeframes of interest as indicated by the event data.

In one or more embodiments, a computer may use image analysis of a videoto assist with synchronization of the video with event data and motionanalysis data. For example, motion analysis data may indicate a strongphysical shock (detected, for instance, using accelerometers) that comesfor instance from the striking of a ball like a baseball or a golf ball,or from the landing of a skateboard after a jump. The computer mayanalyze the images from a video to locate the frame where this shockoccurs. For example, a video that records a golf ball may use imageanalysis to detect in the video stream when the ball starts moving; thefirst frame with motion of the golf ball is the first frame after theimpact with the club, and can then be synchronized with the shock in thecorresponding motion analysis data. This is illustrated in FIG. 24 whereimage analysis of the video identifies golf ball 2401. The frame whereball 2401 starts moving, indicated in the example as Impact Frame 34,can be matched to a specific point in the motion analysis data thatshows the shock of impact. These video and motion data frames can beused as key frames; from these key frames the video frames thatcorrespond most closely to the start and end of an event can be derived.

In one or more embodiments, a computer may use image analysis of a videoto generate a metric from an object within the video. This metric mayfor instance measure some aspect of the motion of the object. Suchmetrics derived from image analysis may be used in addition to or inconjunction with metrics obtained from motion analysis of data frommotion sensors. In some embodiments image analysis may use any ofseveral techniques known in the art to locate the pixels associated withan object of interest. For instance, certain objects may be known tohave specific colors, textures, or shapes, and these characteristics canbe used to locate the objects in video frames. As an example, a golfball may be known to be approximately round, white, and of textureassociate with the ball's materials. Using these characteristics imageanalysis can locate a golf ball in a video frame. Using multiple videoframes the approximate speed and rotation of the golf ball could becalculated. For instance, assuming a stationary or almost stationarycamera, the location of the golf ball in three-dimensional space can beestimated based on the ball's location in the video frame and based onits size. The location in the frame gives the projection of the ball'slocation onto the image plane, and the size provides the depth of theball relative to the camera. By using the ball's location in multipleframes, and by using the frame rate which gives the time differencebetween frames, the ball's velocity can be estimated.

FIG. 24 illustrates this process where golf ball is at location 2401 inframe 2403, and location 2402 in frame 2404. The golf ball has an iconthat can be used to measure the ball's distance from the camera and itsrotation. The velocity of the ball can be calculated using the distancemoved between frames and the time gap between frames. As a simpleexample if the ball's size does not change appreciably between frames,the pixel difference between the ball's locations 2402 and 2401 can betranslated to distance using the camera's field of view and the ball'sapparent size. The frame difference shown in the example is 2 frames(Frame 39 to Frame 41), which can be converted to time based on theframe rate of the camera. Velocity can then be calculated as the ratioof distance to time.

In one or more embodiments, a computer can access previously storedevent data or motion analysis data to display comparisons between a newevent and one or more previous events. These comparisons can be for thesame user and same equipment over time, or between different users anddifferent equipment. These comparisons can provide users with feedbackon their changes in performance, and can provide benchmarks againstother users or users of other types or models of equipment. As anillustration, FIG. 1D shows device 101 receiving event data associatedwith users 150 and 152. This data is transmitted to computer 105 fordisplay and comparison. A user 151 can compare performance of user 150and 152, and can track performance of each user over time.

FIGS. 1F and 1G illustrate an embodiment of the system that enablesbroadcasting images with augmented motion data including at least onecamera 103, 104, configured to receive images associated with orotherwise containing at least one motion capture element 111, a computer140, and a wireless communication interface 106 configured to receivemotion capture data from the at least one motion capture element. In oneor more embodiments, the computer 140 is coupled with the wirelesscommunication interface 106 and the at least one camera, and thecomputer 140 is configured to receive the motion capture data after acommunications link to the at least one motion capture element 111 isavailable and capable of receiving information for example as shown inFIG. 1F, and FIG. 1G at 1191. Embodiments also may receive the motioncapture data after an event or periodically request the motion capturedata at 1192 of FIG. 1G as per FIG. 1F from the at least one motioncapture element 111 as per FIG. 1. This enables the system to withstandcommunication link outages, and even enables the synchronization ofvideo with motion capture data in time at a later point in time, forexample once the motion capture element is in range of the wirelessreceiver. Embodiments may receive motion capture data from at least onemotion capture element 111, for example from one user 150 or multipleusers 150, 151, 152 or both. One or more embodiments also may recognizethe at least one motion capture element 111 associated with a user 150or piece of equipment 110 and associate the at least one motion captureelement 111 with assigned locations on the user 150 or the piece ofequipment 110 of FIG. 1G, at 1193 of FIG. 1G. For example, when a userperforms a motion event, such as swinging, hitting, striking, or anyother type of motion-related activity, the system is able to associatethe motion event with locations on the user, or equipment such as a golfclub, racket, bat, glove, or any other object, to recognize, oridentify, the at least one motion capture element. Embodiments may alsoreceive data associated with the at least one motion capture element 111via the wireless communication interface at 1194 as per FIG. 1G, andalso may receive one or more images of the user associated with themotion capture element at 1195 of FIG. 1G from the at least one camera103, 104. Such data and images allow the system to, for example, obtainan array of information associated with users, equipment, and eventsand/or to output various performance elements therefrom. One or moreembodiments may also analyze the data to form motion analysis data at1196 of FIG. 1G. Motion analysis data, for example, allows the system toobtain and/or output computer performance information to for examplebroadcast to the users, to viewers, coaches, referees, networks, and anyother element capable of receiving such information. Motion analysisdata for example may show motion related quantitative data in agraphical or other easy to understand viewing format to make the datamore understandable to the user than for example pure numerical lists ofacceleration data. For example, as shown in FIG. 1G, embodiments of theinvention may also at 1197, draw a three-dimensional overlay onto atleast one of the one or more images of the user, a rating onto at leastone of the one or more images of the user, at least one power factorvalue onto at least one of the one or more images of the user, acalculated ball flight path onto at least one of the one or more imagesof the user, a time line showing points in time along a time axis wherepeak values occur onto at least one of the one or more images of theuser, an impact location of a ball on the piece of equipment onto atleast one of the one or more images of the user, a slow motion displayof the user shown from around the user at various angles at normal speedonto at least one of the one or more images of the user, or anycombination thereof associated with the motion analysis data. One ormore embodiments may also broadcast the images at 1198, to amultiplicity of display devices including television 143, mobile devices101, 102, 102 a, 102 b, computer 105, and/or to the Internet 171. Forexample, the multiplicity of display devices may include televisions,mobile devices, or a combination of both televisions and mobile devices,or any other devices configured to display images.

FIG. 1H shows an embodiment of the processing that occurs on thecomputer. In one or more embodiments the application is configured toprompt a first user to move the motion capture sensor to a firstlocation at 1181 and accept a first motion capture data from the motioncapture sensor at the first location via the wireless communicationinterface, prompt the first user to move the motion capture sensor to asecond location or rotation at 1182, accept a second motion capture dataor rotation from the motion capture sensor at the second location viathe wireless communication interface, calculate a distance or rotationat 1183 between the first and second location or rotation based on thefirst and second motion capture data. The distance may include a heightor an arm length, or a torso length, or a leg length, or a wrist tofloor measurement, or a hand size or longest finger size or both thehand size and longest finger size of the first user, or any combinationthereof or any other dimension or length associated with the first user.Distances may be calculated by position differences, or by integratingvelocity or doubly integrating acceleration, or in any other mannerdetermining how far apart or how much rotation has occurred depending onthe types of internal sensors utilized in the motion capture sensor asone skilled in the art will appreciate. For example, embodiments of theinvention may prompt the user to hold the motion capture sensor in theuser's hand and hold the hand on top of the user's head and then promptthe user to place the sensor on the ground, to calculate the distancetherebetween, i.e., the height of the user. In another example, thesystem may prompt the user to hold the sensor in the hand, for exampleafter decoupling the sensor from a golf club and then prompt the user toplace the sensor on the ground. The system then calculates the distanceas the “wrist to floor measurement”, which is commonly used in sizinggolf clubs for example. Embodiments of the system may also prompt theuser to move the sensor from the side of the user to various positionsor rotational values, for example to rotate the sensor while at orthrough various positions to calculate the range of motion, for examplethrough flexion, extension, abduction, adduction, lateral rotation,medial rotation, etc. Any of these characteristics, dimensions,distances, lengths or other parameters may be stored in Table 180 ashown in FIG. 1B and associated with the particular user. In one or moreembodiments, the application is further configured to prompt the firstuser to couple the motion capture sensor to a piece of equipment at 1184and prompt the first user to move the piece of equipment through amovement at 1185, for example at the speed intended to be utilized whenplaying a particular sport or executing a particular movement associatedwith a piece of sporting equipment. The application is furtherconfigured to accept a third motion capture data from the motion capturesensor for the movement via the wireless communication interface andcalculate a speed for the movement at 1186 based on the third motioncapture data. In one or more embodiments, the application is configuredto calculate a correlation at 1187 between the distance and the speedfor the first user with respect to a plurality of other users andpresent information associated with an optimally fit or sized piece ofequipment associated with other users. For example, the system maychoose a second user having a maximum value correlation or correlationto the first user within a particular range, for example at least withthe distance and the speed of the first user. The system may then searchthrough the closest parameter users and choose the one with the maximumor minimum performance or score or distance of hitting, etc., and selectthe make/model of the piece of equipment for presentation to the user.For example, one such algorithm may for example provide a list of makeand model of the lowest scoring golf shaft, or longest hitting baseballbat associated with a similar size/range of motion/speed user.Embodiments of the user may use the speed of the user through motions orthe speed of the equipment through motions or both in correlationcalculations for example. The information for the best performingmake/model and size of the piece of equipment is presented to the userat 1188.

In one or more embodiments, the microcontroller coupled to a motioncapture element may communicate with other motion capture sensors tocoordinate the capture of event data. The microcontroller may transmit astart of event notification to another motion capture sensor to triggerthat other sensor to also capture event data. The other sensor may saveits data locally for later upload, or it may transmit its event data viaan open communication link to a computer while the event occurs. Thesetechniques provide a type of master-slave architecture where one sensorcan act as a master and can coordinate a network of slave sensors.

In one or more embodiments of the invention, a computer may use eventdata to discover cameras that can capture or may have captured video ofthe event. Such cameras need to be proximal to the location of theevent, and they need to be oriented in the correct direction to view theevent. In some systems the number, location, and orientation of camerasis not known in advance and must be determined dynamically. As an eventoccurs, a computer receiving event data can broadcast a request to anycameras in the vicinity of the event or oriented to view the event. Thisrequest may for example instruct the cameras to record event video andto save event video. The computer may then request video from theseproximal and correctly oriented cameras after the event. This isillustrated in FIG. 1 where computer 160 may receive notification of anevent start from motion capture element 111. Computer 160 may broadcasta request to all cameras in the vicinity such as 103, 104, 130, 130 a,and 130 b. As an example, cameras 103 and 130 may be proximal andcorrectly oriented to view the event; they will record video. Camera 104may be too far away, and cameras 130 a and 130 b may be close enough butnot aiming at the event; these cameras will not record video.

In some embodiments one or more videos may be available on one or morecomputers (such as servers 152, or cloud services) and may be correlatedlater with event data. In these embodiments, a computer such as 152 maysearch for stored videos that were in the correct location andorientation to view an event. The computer could then retrieve theappropriate videos and combine them with event data to form a compositeview of the event with video from multiple positions and angles.

In one or more embodiments, a computer may obtain sensor values fromother sensors, such as the at least one other sensor, in addition tomotion capture sensors, where these other sensors may be locatedproximal to an event and provide other useful data associated with theevent. For example, such other sensors may sense various combinations oftemperature, humidity, wind, elevation, light, sound and physiologicalmetrics (like a heartbeat or heart rate). The computer may retrieve, orlocally capture, these other values and save them, for example alongwith the event data and the motion analysis data, to generate anextended record of the event during the timespan from the event start tothe event stop.

In one or more embodiments, the types of events detected, monitored, andanalyzed by the microprocessor, the computer, or both, may includevarious types of important motion events for a user, a piece ofequipment, or a mobile device. These important events may includecritical or urgent medical conditions or indicators of health. Some suchevent types may include motions indicative of standing, walking,falling, heat stroke, a seizure, violent shaking, a concussion, acollision, abnormal gait, and abnormal or non-existent breathing.Combinations of these event types may also be detected, monitored, oranalyzed.

In one or more embodiments, the computer 160 of FIG. 1 may be embeddedin any device, including for example, without limitation, a mobiledevice, a mobile phone, a smart phone, a smart watch, a camera, a laptopcomputer, a notebook computer, a table computer, a desktop computer, ora server computer. Any device that may receive data from one or moresensors or one or more cameras, and process this data, may function asthe computer 160. In one or more embodiments, the computer 160 may be adistributed system with components embedded in several devices, wherethese components communicate and interact to carry out the functions ofthe computer. These components may be any combination of devices,including the devices listed above. For example, in one or moreembodiments the computer 160 may include a mobile phone and servercomputer combination, where the mobile phone initially receives sensordata and detects events, and then forwards event data to a servercomputer for motion analysis. Embodiments may use distributed processingacross devices in any desired manner to implement the functions ofcomputer 160. Moreover, in one or more embodiments the computer 160 orportions of the computer 160 may be embedded in other elements of thesystem. For example, the computer 160 may be embedded in one of thecameras like camera 104. In one or more embodiments the computer 160,the motion capture element 111, and the camera 104 may all be physicallyintegrated into a single device, and they may communicate using localbus communication to exchange data. For example, in one or moreembodiments computer 160, motion capture element 111, and camera 104 maybe combined to form an intelligent motion-sensing camera that canrecognize events and analyze motion. Such an intelligent motion-sensingcamera may be mounted for example on a helmet, on goggles, on a piece ofsports equipment, or on any other equipment. In one or more embodiments,the computer 160 may include multiple processors that collaborate toimplement event detection and motion analysis. For example, one or moreembodiments may include a camera with an integrated motion captureelement and a processor, where the camera captures video, the motioncapture element measures motion, and the processor detects events. Theprocessor that detects events may then for example generate asynchronized event video, forward this synchronized event video to amobile device such as 120 and a database such as 172, and then discardvideo from the camera 104 that is outside the event timeframe associatedwith the synchronized event video. Mobile device 120 may for exampleinclude another processor that receives the synchronized event video,optionally further analyzes it, and displays it on the mobile devicescreen.

In at least one embodiment, the at least one motion capture element 111may be contained within a motion capture element mount, a mobile device,a mobile phone, a smart phone, a smart watch, a camera, a laptopcomputer, a notebook computer, a tablet computer, a desktop computer, aserver computer or any combination thereof.

In one or more embodiments, motion capture element 111 may use anysensor or combination of sensors to detect events. For example, in oneor more embodiments, motion capture 111 may include or contain anaccelerometer, and recognition of events may for example includecomparing accelerometer values to a threshold value; high accelerationvalues may correspond to high forces acting on the motion captureelement, and thus they may be indicative of events of interest. Forexample, in an embodiment used to monitor motion of an athlete, highacceleration values may correspond to rapid changes in speed ordirection of motion; these changes may be events of primary interest insome embodiments. Video captured during time periods of highacceleration may for example be selected for highlight reels or failreels, and other video may be discarded. In one or more embodiments thatinclude an accelerometer, recognition of events may include comparingchanges in acceleration over time to a threshold; rapid changes in aspecified time interval may for example indicate shocks or impacts orother rapid movements that correspond to desired events.

In one or more embodiments, sensor data may be collected and combinedwith media obtained from servers to detect and analyze events. The mediamay then be combined with the sensor data and reposted to servers, suchas social media sites, as integrated, media-rich and data-rich recordsof the event. Media from servers may include for example, withoutlimitation, text, audio, images, and video. Sensor data may include forexample, without limitation, motion data, temperature data, altitudedata, heart rate data, or more generally any sensor informationassociated with a user or with a piece of equipment. FIG. 25 illustratesan embodiment of the system that combines sensor data analysis and mediaanalysis for earthquake detection. Detection of earthquakes is anillustrative example; embodiments of the system may use any types ofsensor data and media to detect and analyze any desired events,including for example, without limitation personal events, group events,environmental events, public events, medical events, sports events,entertainment events, political events, crime events, or disasterevents.

In FIG. 25, a user is equipped with three sensors: sensor 2501 is amotion sensor; 2502 is a heart rate sensor; and sensor 2503 is aposition sensor with a clock. These sensors may be held in one physicalpackage or mount or multiple packages or mounts in the same location ona user or in multiple locations. One or more embodiments may use anysensor or any combination of sensors to collect data about one or moreusers or pieces of equipment. Sensors may be standalone devices, or theymay be embedded for example in mobile phones, smart watches, or anyother devices. Sensors may also be near a user and sensor data may beobtained through a network connection associated with one or more of thesensors or computer associated with the user (see FIG. 1A for topologyof sensors and sensor data that the system may obtain locally or overthe network). In the embodiment shown in FIG. 25, sensor 2503 may be forexample embedded in a smart watch equipped a GPS. Heart rate data 2512from sensor 2502, acceleration data 2511 from motion sensor 2501, andtime and location information 2513 from sensor 2503 are sent to computeror mobile device 101 for analysis. Alternatively, the mobile device maycontain all or any portion of the sensors or obtain any of the sensordata internally or over a network connection. In addition, the computermay be collocated with sensor 2502, for example in a smart watch ormobile phone. Mobile device 101 is illustrative; embodiments may use anycomputer or collection of computers to receive data and detect events.These computers may include for example, without limitation, a mobiledevice, a mobile phone, a smart phone, a smart watch, a camera, smartglasses, a laptop computer, a notebook computer, a tablet computer, adesktop computer, and a server computer.

In the example of FIG. 25 the mobile device 101 is configured to scanfor a set of event types, including but not limited to earthquake eventsfor example. Earthquake event detection includes comparison of sensordata to a sensor earthquake signature 2520, and comparison of mediainformation to a media earthquake signature 2550. Embodiments may useany desired signatures for one or more events. Sensor data signaturesfor events used by one or more embodiments may include for example,without limitation, sensor values exceeding one or more thresholds orfalling into or out of one or more ranges, trends in values exceedingcertain thresholds for rates of change, and combinations of values frommultiple sensors falling into or out of certain multidimensional ranges.In FIG. 25, the rapid increase in heart rate shown in 2512 is indicativeof an event, which may be an earthquake for example. The rapid increasein acceleration 2511 is also indicative of an earthquake. Based on thesetwo signatures, device 101 may for example determine that a sensorearthquake signature has been located. In one or more embodiments,sensor data from multiple users with at least some of the sensors may beutilized by any computer such as computer 101 to determine if theacceleration 2511 is observed by multiple sensors, even if slightly timeshifted based on location and time to determine that an earthquake haspotentially occurred.

Computer 101 may also scan media from one or more servers to confirm theevent. Embodiments may obtain media data from any type or types ofservers, including for example, without limitation, an email server, asocial media site, a photo sharing site, a video sharing site, a blog, awiki, a database, a newsgroup, an RSS server, a multimedia repository, adocument repository, a text message server, and a Twitter® server. Inthe example shown in FIG. 25, computer or mobile device 101 scans mediaon two servers: a text message server 2530 that provides a log of textmessages sent and received, and a social media website 2540 that allowsusers to post text and images to their personal home pages. The textmessages on 2530 and postings on 2540 are not necessarily associatedwith the user wearing sensors 2501, 2502, and 2503; embodiments of thesystem may access any servers to obtain media from any sources. Mediaare compared to media earthquake signature 2550. Embodiments may use anydesired media signatures for events, including for example, withoutlimitation, frequencies of selected keywords or key phrases in text,rates of media postings or updates on selected servers, appearance ofspecific images or videos matching any specified characteristics,urgency of messages sent, patterns in sender and receiver networks formessages, and patterns in poster and viewer networks for social mediasites. In FIG. 25, the media earthquake signature 2550 includesappearance of key works like 2531 “shaking” and 2541 “falling down” inthe text messages and home page, respectively. The media earthquakesignature may also include analysis of photos or videos for images thatare characteristic of an earthquake, such as images of buildings swayingor falling for example. In FIG. 25, image 2542 shows a falling monumentthat is consistent with the media earthquake signature 2550. Keywordsmay be utilized to eliminate false positives for images showing similaritems, for example “movie” in case someone posted an image or video notrelated to a current event for example. In one or more embodiments, thescreen on mobile device 101 and others in the vicinity may flash or makesound or both to indicate an emergency. If the event was a truck drivenby a terrorist and accelerations indicated movement by other proximalmobile devices with accelerometers, and social media indicated keywords,such as “terror”, “attack”, “run!”, etc., then the screens may flashlocally to alert users that cannot see the impending event for example.Alternatively, fake news can be detected by analyzing the social mediaand sensors to verify the media post. For example if a user posts that agiven person is running currently and the another post or associatedsensor data shows that the user is skiing or at altitude, then the postcan be flagged for potential fake news categorization.

One or more embodiments may generate integrated event records thatcombine sensor data with media describing the event, such as photos,videos, audio, or text commentaries. The media may be obtained forexample from servers such as social media sites, from sensors associatedwith the system such as local cameras, or from combinations thereof. Oneor more embodiments may curate this data, including the media fromsocial media sites, to generate highlights of an event. The curated,integrated event records may combine media and data in any desiredmanner, including for example through overlays of data onto photos orvideos. Integrated event records may contain all or a selected subset ofthe media retrieved from servers, along with all or a selected subset ofthe sensor data, metrics, and analyses of the event. Integrated eventrecords may be reposted to social media sites or broadcast to otherusers.

One or more embodiments may correlate sensor data and media by time,location, or both, as part of event detection and analysis. For example,earthquakes occur at specific points in time and at specific locations;therefore, two shaking signatures separated by a 100 day time intervalare likely not related, while events separated by a relatively smalltime interval, e.g., minutes and perhaps within a given predefined rangefor example based on the event type, e.g., miles in this case, are morelikely to indicate a prospective related event. In FIG. 25, sensor 2503provides the time and location 2513 of the user, which may be correlatedwith the sensor data 2511 and 2512. This time and location data may beused in the searches of servers 2530 and 2540 for media that may confirmthe event, for example within predefined thresholds for time andlocation, and optionally based on event type. One or more embodimentsmay group sensor data and media by time and location to determine ifthere are correlated clusters of information that represent events at aconsistent time and location. The scale for clustering in time andlocation may depend upon the event. For example, an earthquake may lastseveral minutes, but it is unlikely to last several weeks. It may alsocover a wide area, but it is unlikely to have an effect over severalthousand miles.

In FIG. 25, the text message 2531 and the posting 2541 both occur withinone minute of the sensor data 2511, 2512, and 2513; therefore, themobile device 101 correlates the media with the sensor data. Since thesensor data match sensor signature 2520 and the media match mediasignature 2550, the mobile device confirms an earthquake event 2560.

The text analysis of text messages and postings in FIG. 25 uses a simplemedia signature for an event based on the appearance of selectedkeywords. One or more embodiments may employ any text processing or textanalysis techniques to determine the extent to which a textualinformation source matches an event signature. One or more embodimentsmay be configured to scan for multiple types of events; in theseembodiments, textual analysis may include generating a relative scorefor various event types based on the words located in textualinformation sources. FIG. 26 illustrates an embodiment of the systemthat uses an event-keyword weighting table 2620 to determine the mostlikely event based on text analysis. Each keyword is rated for eachevent of interest to determine an event-keyword weight. In this example,the keyword 2621 (“Air”) has an event-keyword weight for four possibleevents: Touchdown, Crash, Earthquake, and Jump. These weights may forexample reflect the relative likelihood that messages or textsdescribing these events include that keyword. Weights may be determinedin any desired manner: they may be based on historical analysis ofdocuments or messages, for example; they may be configured based onjudgment; and they may be developed using machine learning algorithmsfrom training sets. In the example shown in FIG. 25, event 2601 isobserved by several users that send tweets about the event; these tweetsare available on server 2610. The system scans these tweets (potentiallyusing event times and locations as well to limit the search) andidentifies three messages containing keywords. For example, the firstmessage 2611 contains the keyword 2621 from table 2620. The weights ofthe keywords for each event are added, generating event scores 2630. Inthis example, the “Jump” event has the highest score, so the systemdetermines that this is the most likely event. One or more embodimentsmay use scoring or weighting techniques to assess probabilities thatvarious events have occurred, and may use probability thresholds toconfirm events. One or more embodiments may use Bayesian techniques, forexample, to update event probabilities based on additional informationfrom other media servers or from sensor data. In addition, the sensor orcomputer associated with the computer that detects a potential event maybroadcast to nearby cameras and/or computers for any related video forexample during the duration of the event, including any pre-event orpost-event window of time. Users that are on a ski lift for examplegenerating video of the epic fail, may thus receive a message requestingany video near the location and time of the event. Direction of thecamera or field of view may be utilized to filter event videos from thevarious other users at the computer or at the other user's computers.Thus, the event videos may be automatically curated or otherwisetransferred and obtained without the non-event video outside of the timewindow of the event. In addition, the video or other media such as text,audio or image data, may be trimmed automatically on the variouscomputers in the system in real-time in post processing to discardnon-event related video. In one or more embodiments, the computer mayquery the user with the event videos and request instructions to discardthe remaining non-event video. The event videos may be transferred muchmore efficiently without the non-event video data and the transfer timesand storage requirements maybe 2 to 3 orders of magnitude lower in manycases.

One or more embodiments of the system may use a multi-stage eventdetection methodology that first determines that a prospective event hasoccurred, and then analyzes additional sensor data or media data todetermine if the prospective event was a valid event or a false positiveevent. FIG. 27 illustrates an example of a multi-stage event detectionsystem. For illustration, a falling anvil is equipped with an altitudesensor 2701, and a rabbit is also equipped with an altitude sensor 2702.The system receives sensor data samples from 2701 and 2702 and combinesthem to form graph 2710. In one or more embodiments, additionalprocessing may be desired to synchronize the clocks of the two sensors2701 and 2702; (see FIG. 1E for examples of time synchronization thatthe system may utilize). Analysis 2710 of the relative altitude predictsa prospective collision event 2720 at time 2711 when the altitudes ofthe two objects coincide. However, this analysis only takes into accountthe vertical dimension measured by the altitude sensor; for a collisionto occur the objects must be at the same three-dimensional coordinatesat the same time. FIG. 27 illustrates two examples of using additionalinformation to determine if prospective event 2720 is a valid event or afalse positive. One technique used by one or more embodiments is toreview media information from one or more servers to confirm orinvalidate the prospective event. For example, the system may perform asearch 2730 to locate objects 2731 and 2732 in media on availableservers, such as the server 2740 that contains videos shared by users.For example, the shape, size, color, or other visual characteristics ofthe objects 2731 and 2732 may be known when the sensors 2701 and 2702are installed. In this example, video 2741 is located that contains theobjects, and analysis of the frames shows that a collision did notoccur; thus, the system can determine that the event was a falsepositive 2750. One or more embodiments may use any criteria to searchservers for media that may confirm or invalidate a prospective event,and may analyze these media using any techniques such as for exampleimage analysis, text analysis, or pattern recognition. The lower rightof FIG. 27 illustrates another example that uses additional sensorinformation to differentiate between a prospective event and a validevent. In this example, the anvil and the rabbit are equipped withhorizontal accelerometers 2761 and 2762, respectively. Using techniquesknown in the art, horizontal acceleration is integrated to formhorizontal positions 2770 of the objects over time. By combining thevertical trajectories 2710 and the horizontal trajectories 2770, thesystem can determine that at time 2711 the horizontal positions of thetwo objects are different; thus, the system determines that theprospective event 2720 is a false positive 2780. These examples areillustrative; embodiments may use any combination of additional sensordata and media information to confirm or invalidate a prospective event.For example, media servers may be checked and if there are posts thatdetermine that some collision almost occurred, such as “wow that wasclose”, etc., (see FIG. 26 for a crash scenario with media keyword scorechecking), or did not occur at 2750. If a post indicates that an eventoccurred, while the sensor data shows otherwise, then the post can beflagged as a potential fake news story.

One or more embodiments may use additional sensor data to determine atype of activity that was performed or a type of equipment that was usedwhen sensor data was captured. FIG. 28 illustrates an example of a userthat may use a motion sensor for either snowboarding or surfing. Motionsensor 2501 a is attached to snowboard 2810, and motion sensor 2501 b isattached to surfboard 2820. The motion sensors may for example includean accelerometer, a rate gyroscope, and potentially other sensors todetect motion, position or orientation. In one or more embodiments, thedevices 2501 a and 2501 b may be identical, and the user may be able toinstall this device on either a snowboard or a surfboard. Based on themotion sensor data, the speed of the user over time is calculated by thesystem. The speed chart 2811 for snowboarding and the speed chart 2821for surfing are similar; therefore, it may be difficult or impossible todetermine from the motion data alone which activity is associated withthe data. In this example, sensors 2501 a and 2501 b also include atemperature sensor and an altitude sensor. The snowboarding activitygenerates temperature and altitude data 2812; the surfing activitygenerates temperature and altitude data 2822. The system is configuredwith typical signatures 2830 for temperature and altitude for surfingand snowboarding. In this illustrative example, the typical temperatureranges and altitude ranges for the two activities do not overlap; thus,it is straightforward to determine the activity and the type ofequipment using the temperature and altitude data. The low temperatureand high altitude 2812 combined with the signatures 2830 indicateactivity and equipment 2813 for snowboarding the high temperature andlow altitude 2822 combined with the signatures 2830 indicate activityand equipment 2823 for surfing. One or more embodiments may use anyadditional sensor data, not limited to temperature and altitude, todetermine a type of activity, a type of equipment, or both.

One or more embodiments of the system may collect data from multiplesensors attached to multiple users or to multiple pieces of equipment,and analyze this data to detect events involving these multiple users ormultiple pieces of equipment. FIG. 29 illustrates an example withsensors attached to people in an audience. Several, but not necessarilyall, of the members of the audience have sensors that in this examplemeasure motion, time, and location. These sensors may for example beembedded in mobile devices carried or worn by these users, such as smartphones or smart watches. As shown, at least 4 users have sensors 2901 a,2902, 2903, and 2904 a. The system collects motion data and determinesthe vertical velocity (v_(z)) of each user over time, for example 2911,2912, and 2913. While the users are seated, the vertical velocity iseffectively zero or very small; when they stand, the vertical velocityincreases, and then decreases back to zero. In this illustrativeexample, the system monitors the sensor data for this signature of auser standing, and determines the time at which the standing motioncompletes. For example, the times for the completion of standing for theusers with sensors 2901 a, 2902, and 2903 are 2921, 2922, and 2923,respectively. The system also monitors the location data 2931, 2932, and2933 from the sensors 2901 a, 2902, and 2903, respectively. Locationdata shown here is encoded as latitude and longitude; one or moreembodiments may use any method for determining and representing partialor complete location data associated with any sensor.

The illustrative system shown in FIG. 29 is configured to detect astanding ovation event from the audience. The signature of this event isthat a critical number of users in the same audience stand up atapproximately the same time. This signature is for illustration; one ormore embodiments may use any desired signatures of sensor data to detectone or more events. Because the system may monitor a large number ofsensors, including sensors from users in different locations, one ormore embodiments may correlate sensor data by location and by time todetermine collective events involving multiple users. As shown in FIG.29, one approach to correlating sensor data by time and location is tomonitor for clusters of individual events (from a single sensor) thatare close in both time and location. Chart 2940 shows that theindividual standing events for the three users are clustered in time andin longitude. For illustration, we show only the longitude dimension oflocation and use an example where latitudes are identical. One or moreembodiments may use any or all spatial dimensions and time to clustersensor data to detect events. Cluster 2941 of closely spaced individualsensor events contains three users, corresponding to sensors 2901 a,2902, and 2903. The system is configured with a critical threshold 2942of the number of users that must stand approximately at the same time(and in approximately at the same location) in order to define astanding ovation event. In this example, the critical count is three, sothe system declares a standing ovation event and sends a message 2950publishing this event. In addition, other sensors including soundsensors may be utilized to characterize the event as an ovation orbooing. Any other physiological sensors including heart rate sensors mayalso be utilized to determine the qualitative measure of the event, inthis case a highly emotional standing ovation if the heart rates areover a predefined threshold. Furthermore, blog sites, text messages orother social media sites may be checked to see if the event correlateswith the motion sensor, additional sensors such as sound or heart rateor both, to determine whether to publish the event, for example on asocial media website or other Internet site for example (see FIG. 25 foran example of checking a website for corroborating evidence thatembodiments of the system may utilize).

FIG. 29 illustrates an embodiment of the system that detects an eventusing a threshold for the number of individual sensor events occurringwithin a cluster of closely spaced time and location. FIG. 30illustrates an embodiment that detects an event using an aggregatemetric across sensors rather than comparing a count to threshold value.In this embodiment, a potentially large number of users are equippedwith motion and position sensors such as sensor 3001 a, 3001 b worn by auser, and smart phone 3002 a, 3002 b carried by a user. Each sensorprovides a data feed including the user's latitude, longitude, andspeed. For example, the sensor may include a GPS to track latitude andlongitude, and an inertial sensor that may be used to determine theuser's speed. In this illustrative system, sensors are partitioned intolocal areas based on the user's current latitude and longitude, and theaverage speed 3010 of users in each local area is calculated andmonitored. When the system detects an abrupt increase 3020 in theaverage speed of users in an area, it determines that a “major incident”3030 has occurred at that local area, for example at 123 Elm St. Thisevent may be published for example as an email message, a text message,a broadcast message to users in the vicinity, a tweet, a posting on asocial media site, or an alert to an emergency service. In this example,the sensor data is not sufficient to characterize the event precisely;for example, instead of a fire as shown in FIG. 30, other events thatmight cause users to start moving rapidly might be an earthquake, or aterrorist attack. However, the information that some major incident hasoccurred at this location may be of significant use to manyorganizations and users, such as first responders. Moreover, embodimentsof the system may be able to detect such events instantaneously bymonitoring sensor values continuously. The average speed metric used inFIG. 30 is for illustration; one or more embodiments may calculate anydesired aggregate metrics from multiple sensor data feeds, and may usethese metrics in any desired manner to detect and characterize events.One or more embodiments may combine the techniques illustrated in FIGS.29 and 30 in any desired manner; for example, one or more embodimentsmay analyze individual sensor data to determine individual events,cluster the number of individual events by time and location, and thencalculate an aggregate metric for each cluster to determine if anoverall event has occurred. One or more embodiments may assign differentweights to individual events based on their sensor data for example, anduse weighted sums rather than raw counts compared to threshold values todetect events. Any method of combining sensor data from multiple sensorsto detect events is in keeping with the spirit of the invention. Asshown, with users travelling away from a given location, the locationmay be determined and any associated sound or atmospheric sensors suchas CO2 sensors located near the location may be utilized to confirm theevent as a fire. Automatic emergency messages may be sent by computer3002 a, which may also broadcast for any pictures or video around thelocation and time that the event was detected.

Sensor events associated with environmental, physiological and motioncapture sensors may thus be confirmed with text, audio, image or videodata or any combination thereof, including social media posts forexample to detect and confirm events, and curate media or otherwisestore concise event videos or other media in real-time or nearreal-time. For example, one or more embodiments may access social mediasites to retrieve all photos and videos associated with an event,potentially by matching time and location data in the photos and videoto sensor data timestamps and location stamps. The retrieved media maythen be curated or organized to generate integrated event records thatinclude all or a selected subset of the media. In addition, social mediasites may utilize embodiments of the invention to later confirm eventsusing environmental, physiological and motion capture sensors accordingto one or more embodiments of the invention, for example by filteringevents based on time or location or both in combination with embodimentsof the invention. Ranking and reputation of posts or other media mayalso be utilized to filter or publish events in combination with one ormore embodiments of the invention. Multiple sources of information forexample associated with different users or pieces of equipment may beutilized to detect or confirm the event. In one or more embodiments, anevent may be detected when no motion is detected and other sensor dataindicates a potential event, for example when a child is in a hot carand no movement is detected with a motion sensor coupled with the child.Events may also be prioritized so that if multiple events are detected,the highest priority event may be processed or otherwise published ortransmitted first.

In one or more embodiments, the sensor and media event detection andtagging system may analyze sensor data to automatically generate orselect one or more tags for an event. Event tags may for example groupevents into categories based on the type of activity involved in theevent. For example, analysis of football events may categorize a play asa running play, a passing play, or a kicking play. For activities thatoccur in multiple stages (such as the four downs of a footballpossession, or the three outs of a baseball inning), tags may indicatethe stage or stages at which the event occurs. For example, a footballplay could be tagged as occurring on third down in the fourth quarter.Tags may identify a scenario or context for an activity or event. Forexample, the context for a football play may include the yards remainingfor first down; thus, a play tag might indicate that it is a third downplay with four yards to go (3^(rd) and 4). Tags may identify one or moreplayers associated with an event; they may also identify the role ofeach player in the event. Tags may identify the time or location anevent. For example, tags for a football play may indicate the yard linethe play starts from, and the clock time remaining in the game orquarter when the play begins. Tags may measure a performance levelassociated with an event, or success or failure of an activity. Forexample, a tag associated with a passing play in football may indicate acomplete pass, incomplete, or an interception. Tags may indicate aresult such as a score or a measurable advancement or setback. Forexample, a football play result tag might indicate the number of yardsgained or lost, and the points scored (if any). Tags may be eitherqualitative or quantitative; they may have categorical, ordinal,interval, or ratio data. Tags may be generic or domain specific. Ageneric tag for example may tag a player motion with a maximumperformance tag to indicate that this is the highest performance forthat player over some time interval (for example “highest jump of thesummer”). Domain specific tags may be based on the rules and activitiesof a particular sport. Thus, for example result tags for a baseballswing might include baseball specific tags such as strike, ball, hitfoul, hit out, or hit safe.

FIG. 31 illustrates an example in which event analysis and taggingsystem 3150 analyzes sensor data for a pitch and the correspondingbaseball swing. Event analysis and tagging is performed for example byeither or both of computer 105 and mobile device 101. Sensors mayinclude for example inertial sensor 111, video camera 103, radar 3171,and light gate 3172. The analysis system 3150 detects the swing, andthen analyzes the sensor data to determine what tags to associate withthe swing event. Tags 3103 identify for example the type of event (an atbat), the player making the swing (Casey), a classification for the typeof pitch (curve ball, as determined from analysis of the shape of theball trajectory), the result of the swing (a hit, as detected byobserving the contact 3161 between the bat 3162 and the ball 3163), anda timestamp for the event (9^(th) inning). These tags are illustrative;one or more embodiments may generate any tag or tags for any activity orevent. The system may store the event tags 3103 in an event database172. Additional information 3102 for the event may also be stored in theevent database, such as for example metrics, sensor data, trajectories,or video.

The event analysis and tagging system 3150 may also scan or analyzemedia from one or more servers or information sources to determine,confirm, or modify event tags 3103. Embodiments may obtain media datafrom any type or types of servers or information sources, including forexample, without limitation, an email server, a social media site, aphoto sharing site, a video sharing site, a blog, a wiki, a database, anewsgroup, an RSS server, a multimedia repository, a documentrepository, a text message server, and a Twitter® server. Media mayinclude for example text, audio, images, or videos related to the event.For example, information on social media servers 3105 may be retrieved3106 over the Internet or otherwise, and analyzed to determine, confirm,or modify event tags 3103. Events stored in the event database may alsobe published 3107 to social media sites 3105, or to any other servers orinformation systems. One or more embodiments may publish any or all dataassociated with an event, including for example metrics, sensor data,trajectories, and video 3102, and event tags 3103.

One or more embodiments may provide capabilities for users to retrieveor filter events based on the event tags generated by the analysissystem. FIG. 32 shows an illustrative user interface 3200 that mayaccess event database 172. A table of events 3201 may be shown, and itmay provide options for querying or filtering based on event tags. Forexample, filters 3202 and 3203 are applied to select events associatedwith player “Casey” and event type “at bat.” One or more embodiments mayprovide any type of event filtering, querying, or reporting. In FIG. 32the user selects row 3204 to see details of this event. The userinterface then displays the tags 3103 that were generated automaticallyby the system for this event. A manual tagging interface 3210 isprovided to allow the user to add additional tags or to edit the tagsgenerated by the system. For example, the user may select a tag name3211 to define a scoring result associated with this event, presumingfor example that the automatic analysis of sensor data is not able inthis case to determine what the scoring result was. The user can thenmanually select or enter the scoring result 3212. The manually selectedtags may then be added to the event record for this event in the eventdatabase 172 when the user hits the Add button 3213 for the new tag ortags. The user interface may show other information associated with theselected event 3204, such as for example metrics 3102 a and video 3220.It may provide a video playback feature with controls 3221, which mayfor example provide options such as 3222 to overlay a trajectory 3223 ofa projectile or other object onto the video. One or more embodiments mayprovide a feature to generate a highlight reel for one or more eventsthat correspond to selected event tags. For example, when a user pressesthe Create Highlight Reel button 3230, the system may retrieve video andrelated information for all of the events 3201 matching the currentfilters, and concatenate the video for all of these events into a singlehighlight video. In one or more embodiments, the highlight reel may beautomatically edited to show only the periods of time with the mostimportant actions. In one or more embodiments, the highlight reel maycontain overlays showing the tags, metrics, or trajectories associatedwith the event. One or more embodiments may provide options for thegeneration or editing of the highlight reel; for example, users may havethe option to order the events in the highlight reel chronologically, orby other tags or metrics. The highlight reel may be stored in eventdatabase 172, and may be published to social media sites 3105.

FIG. 33 illustrates an embodiment that analyzes social media postings toaugment tags for an event. Data from sensors such as inertial sensor 111and video camera 103 is analyzed 3301 by the event analysis and taggingsystem 3150, resulting in initial event tags 3103 a. In thisillustrative example, the sensors 111 and 103 are able to detect thatthe player hit the ball, but are not able to determine the result of thehit. Therefore, event tags 3103 a do not contain a “Swing Result” tagsince the sensor data is insufficient to create this tag. (This exampleis illustrative; in one or more embodiments sensor data may besufficient to determine a swing result or any other information.) Theevent analysis and tagging system 3150 accesses social media sites 3105and analyzes postings 3303 related to the event. For example, the systemmay use the time and location of the event to filter social mediapostings from users near that location who posted near the time of theevent. In this example, the system searches text postings for specifickeywords 3304 to determine the result of the event. Although the sensorsor video may be utilized to indicate that a hit has occurred, socialmedia may be analyzed to determine what type of hit, i.e., event hasactually occurred. For example, based on this text analysis 3302, thesystem determines that the result 3305 is a likely home run; thereforeit adds tag 3306 to the event tags with this result. The augmented eventtags 3103 b may then be stored in the event database and published tosocial media sites. The keyword search shown in FIG. 33 is illustrative;one or more embodiments may use any method to analyze text or othermedia to determine, confirm, or modify event tags. For example, withoutlimitation, one or more embodiments may use natural language processing,pattern matching, Bayesian networks, machine learning, neural networks,or topic models to analyze text or any other information. Embodiments ofthe system yield increased accuracy for event detection not possible ordifficult to determine based on sensor or video data in general. Eventsmay be published onto a social media site or saved in a database forlater analysis, along with any event tags for example.

One or more embodiments may save or transfer or otherwise publish only aportion of a video capture, and discard the remaining frames. FIG. 34illustrates an embodiment with video camera 103 that captures videoframes 3401. The video contains frames 3410 a, 3410 b, and 3410 crelated to an event of interest, which in this example is a hitperformed by batter 3451. The bat is equipped with an inertial sensor111. Data from inertial sensor 111 is analyzed by event analysis andtagging system 3150 to determine the time interval of interest for thehit event. This analysis indicates that only the video frames 3410 a,3410 b, and 3410 c are of interest, and that other frames such as frame3411 should be discarded 3402. The system generates event tags 3103 andsaves the tags and the selected video frames 3403 in event database 172.This information, including the selected video frames, may be publishedfor example to social media sites 3105, e.g., without transferring thenon-event data. The discard operation 3402 may for example erase thediscarded frames from memory, or may command camera 103 to erase theseframes. One or more embodiments may use any information to determinewhat portion of a video capture to keep and what portion to discard,including information from other sensors and information from socialmedia sites or other servers.

In one or more embodiments, data from a sensor or sensors may beintegrated with other information to detect events, to determine periodsof time and/or location ranges when and/or where interesting activitiesoccurred, and to form integrated records of events that may for examplecontain both sensor data and media. The event records generated by thesystem may be published or shared in any desired manner, for example byposting on social media sites or social media services. The eventrecords may be curated to select events or periods of time and/orlocation ranges that are relevant or that are of significant interest.

FIG. 35 illustrates an embodiment that combines and correlates data fromany type or types of sensor with media captures from any type or typesof media capture device. Sensors 3501 may include for example, withoutlimitation, any or all of physiologic sensors 3502, environmentalsensors 3503, motion sensors 3504, physical sensors 3505, and chemicalsensors 3506. These sensor types are illustrative; one or moreembodiments may obtain sensor data from any type or types of sensors toanalyze and detect any type or types of events. Sensors may beassociated with any type or types of objects, and they may measure anyproperty or properties of these objects. Illustrative objects that maybe measured or tracked by sensors associated with one or moreembodiments may include for example, without limitation, a person, agroup of people, a body part of a person or of a group of people, afood, a drink, a plant, an animal, a piece of equipment, a machine, anautomobile, a vehicle, an engine, a building, a room, an area, and abody of water. Illustrative properties that may be measured by sensorsassociated with one or more embodiments include for example, withoutlimitation, acoustic pressure, acoustic power, acoustic frequency,sound, vibration, seismic activity, air flow, fluid flow, mass flow,oxygen level, hydrogen level, ozone level, pH, smoke level, carbondioxide level, carbon monoxide level, chemical composition, ionizationlevel, chemical reaction rate, radiation level, charge, electriccurrent, electric potential, resistance, conductance, capacitance,inductance, impedance, electromagnetic field, electromagnetic frequency,wavelength, Doppler shift, light level, particle count, photon count,amplitude, temperature, moisture, humidity, barometric pressure,pollution level, precipitation level, tide level, wind velocity, mass,weight, density, position, depth, altitude, displacement, proximity,presence, orientation, angle, inclination, tilt, shock, strain, mileage,velocity, speed, angular velocity, acceleration, angular acceleration,force, torque, momentum, revolutions per minute, heart rate, bloodpressure, body temperature, blood composition, body fluid composition,tissue composition, oxygen saturation, and respiration rate.

In the embodiment illustrated in FIG. 35, sensor data from sensors 3501is combined or correlated with media obtained from media capture devices3511. One or more embodiments may integrate sensor data with any type ortypes of media, including for example, without limitation, images 3512,video 3513, sound 3514, virtual reality 3515, and text 3516. Video andimages may include panoramic video and images in one or moreembodiments, including for example 360-degree images or 360-degreevideo. Virtual reality media may include for example, withoutlimitation, media associated with any or all of a virtual realitydisplay, a virtual reality presentation, a virtual reality recording, anaugmented reality display, an augmented reality presentation, and anaugmented reality recording.

Sensor data from sensor or sensors 3501 may be combined with, analyzedwith, or correlated with media from media capture device or devices3511, using for example, an event analyzer 3520. This event analyzer mayfor example correlate the time and date 3522 and the location 3521 ofeach sensor datum and each media capture (or of portions thereof), todetermine whether separately obtained sensor data and media captures mayrepresent the same event. For example, if a media capture occurred atapproximately the same location and same time and date as a sensor datacapture, these may represent a common event. One or more embodiments mayperform this correlation in time and space in any desired manner. Forexample, without limitation, sensor data captures and media captures mayinclude information that identify the time and location of the captures.In one or more embodiments, the information about the time and locationassociated with sensor data or media may be obtained in other ways, forexample via user input or tagging, or by analyzing images to determine alocation where media was captured.

The event analyzer 3520 may combine sensor data and media to formintegrated event records 3530, which may for example include mediacaptures (or portions thereof) annotated with sensor data. Theseintegrated event records may be posted 3531 or shared, for example tosocial media sites or services 3105. In one or more embodiments, theevent analyzer 3520 may also retrieve data such as text, video, virtualreality, images, or audio 3522 from social media sites or services 3105(or from any other sources) to confirm events or to further integrateother information into the integrated event records 3530.

The event analyzer 3520 may include any combination of software andhardware, and may execute on any device or devices. For example, withoutlimitation, the event analyzer may execute on any or all of a mobiledevice, a microprocessor associated with a sensor, a media capturedevice, a tablet, a laptop, a desktop computer, a server computer, asmart phone, a smart watch, smart glasses, or glasses having a processorand/or camera, a wearable device, or a network of any of these devices.In one or more embodiments, an event analyzer may be incorporated into asocial media service or social media site.

FIG. 35 shows an illustrative integrated event record 3532, which mayfor example include a video or audio capture along with sensor datadescribing an event. Frames 3533 may be selected for the integratedevent record. In one or more embodiments, these selected frames may be asubset of the total media captures available, because the event analyzer3520 may curate the media captures to focus on specific periods of time(or specific media captures) that illustrate an event or that illustratespecific activities of interest. The integrated event record 3532 mayinclude a location 3534 and a date and time stamp 3535. An integratedevent record may be associated with multiple locations and multipledates and times, or a range of dates and times. Specific media framessuch as frame 3536 may be tagged to indicate that there are specificactivities of interest associated with that frame. One or more highlightreels such as highlight frames 3537 may be extracted from or indicatedalong with the integrated event record 3532. As a user reviews or playsthe integrated event record, a currently selected or viewed frame 3538may be displayed along with sensor data 3537 associated with this frame,or with any metrics or statistics derived from sensor data.

FIG. 36 shows another illustration of an embodiment that combines mediaand sensor data to form integrated event records. Media 3610 may beobtained for example from media networks 3601. Media networks that aresources for media may include for example, without limitation,Facebook®, WhatsApp®, Facebook Messenger®, QQ®, WeChat®, QZone®,Tumblr®, Instagram®, Twitter®, Baidu Tieba®, Skype®, Viber®, SinaWeibo®, Line®, Snapchat®, Yy®, VKontakte®, Pinterest®, BBM®, LinkedIn®,and Telegram®. In one or more embodiments, media may also be obtaineddirectly from one or more users or user devices. Sensor data 3611 may beobtained for example from sensors 3501. Sensors 3501 may measure forexample, without limitation, properties such as acoustic, sound,vibration, automotive, transportation, chemical, electric current,electric potential, magnetic, radio, flow, fluid velocity, ionizingradiation, subatomic particles, navigational instruments, position,angle, displacement, distance, speed, acceleration, optical, light,imaging, photon, pressure, force, density, level, thermal, heat,temperature, proximity, and presence. Event analyzer 3520 may correlatemedia 3610 and sensor data 3611 by location, time and date, or any otherfactors, to determine which combinations of media and sensor datarepresent common events. For example, in the example shown in FIG. 36,media capture 3620 and sensor data capture 3621 are associated withsimilar times and locations; hence these are combined into integratedevent record 3622. This event record 3622 may be a curated record; forexample, only a portion of media capture 3620 may be included in theintegrated event record. This curated portion of media and sensor datamay correspond for example to highlights of an event, or to specifictypes of activities detected during selected portions of the media andsensor data captures.

One or more embodiments may analyze sensor data, media captures, or anyother information to generate one or more suggestions for a user or agroup of users. Suggestions may be posted for example on a social mediasite, such as user's homepage. FIG. 37 illustrates an example where user3701, who is a golfer, has a motion sensor 3702 integrated into orattached to the user's golf club. This sensor 3702 automatically posts3703 sensor data to a server 3105 (potentially via another device ordevices such as a mobile phone, for example). The system analyzes thisdata 3105 to determine whether there are any suggestions that may beappropriate for user 3701. In one or more embodiments, this analysis mayuse any information, including but not limited to sensor data, todetermine appropriate suggestions. For example, the analysis may usedemographic information known about the user, or may use data mining ofthe user's posts on social media, the user's network of contacts, andthe user's previous activities. In the example shown in FIG. 37, thesystem generates three suggestions 3721 based on an analysis of theuser's golf swing data. These suggestions are posted onto the user'shomepage 3720 for a social media site. Suggestion 3722 containsrecommended friends or contacts for the users; in this example, thesesuggested contacts are for other users who have similar golf swings.Friend or contact suggestions may be made based on any analysis ofsensor data or other information such as user characteristics.Suggestion 3723 provides recommended equipment, which may also be basedon an analysis of the user's sensor data or on analysis of any otherrelevant factors. Suggestion 3724 recommends an activity, in this case atraining camp, that is appropriate for people with characteristicsmatching the sensor data obtained from the user's golf swings. One ormore embodiments may provide any type of suggestion or recommendation toa user, and may communicate the suggestion or recommendation in anymanner, including but not limited to providing the suggestion orrecommendation on a social media site.

While the ideas herein disclosed has been described by means of specificembodiments and applications thereof, numerous modifications andvariations could be made thereto by those skilled in the art withoutdeparting from the scope of the invention set forth in the claims.

What is claimed is:
 1. A system that integrates sensor data and socialmedia comprising: a computer comprising a computer memory; and, a firstcommunication interface configured to obtain data, or event datacomprising one or more values from at least one sensor configured tomeasure a property of an object wherein said one or more values areassociated with one or more of an inertial sensor value associated withan orientation, position, velocity, acceleration, angular velocity,angular acceleration, a physical value, an environmental value, aphysiological value associated with a user or piece of equipment ormobile device associated with the user; wherein said computer is coupledwith said computer memory and is coupled with said first communicationinterface, wherein said computer is configured to receive said data fromsaid first communication interface and analyze said data and recognizean event within said data to determine said event data, or said eventdata from said first communication interface, or both said data and saidevent data from said first communication interface; store said eventdata in said computer memory; obtain an event start time and an eventstop time from said event data; confirm said event recognized by saidcomputer or obtained from said first communication interface for aparticular location and time by analyzing one or more of text, audio,image, and video from a server to create a confirmed event, wherein saidone or more of said text, audio, image, and video comprise one or moreof email messages, voice calls, voicemails, audio recordings, videocalls, video messages, video recordings, text messages, chat messages,postings on social media sites, postings on blogs, and postings onwikis; and, wherein said computer is further configured to generate asuggestion or alert based on analysis of said data, said event data, orboth said data and said event data; transmit said suggestion or saidalert to said user; wherein said suggestion comprises a second user or agroup of users having associated data, associated event data, or bothassociated data and associated event data that is similar to said data,said event data, or both said data and said event data, or a recommendedproduct or a recommended piece of equipment that is appropriate for aperformance level determined from said data, from said event data, orfrom both said data and said event data, or a recommended activity thatis appropriate for a performance level determined from said data, fromsaid event data, or from both said data and said event data.
 2. Thesystem of claim 1, wherein said computer is further configured topublish said confirmed event to a social media site or a social mediaservice.
 3. The system of claim 2, wherein said server comprises saidsocial media site or said social media service.
 4. The system of claim2, wherein said computer is further configured to generate a curatedevent record of said confirmed event; and, publish said curated eventrecord to said social media site or said social media service.
 5. Thesystem of claim 4, wherein said generate said curated event recordcomprises: obtain a media capture; select a portion of said mediacapture occurring between said event start time and said event stoptime; and, include said portion of said media capture in said curatedevent record.
 6. The system of claim 5, wherein said select a portion ofsaid media capture occurring between said event start time and saidevent stop time comprises: identify one or more highlights based on oneor both of said data and said event data; and, select said portion ofsaid media capture to include said one or more highlights.
 7. The systemof claim 5, wherein said media capture comprises one or more of animage, a video, and an audio.
 8. The system of claim 5, wherein saidcomputer is further configured to discard or instruct another computerto discard or instruct a media capture device to discard at least asecond portion of said media capture that occurs outside of a timespanfrom said event start time to said event stop time.
 9. The system ofclaim 7, wherein said media capture comprises one or more of a panoramicimage and a panoramic video.
 10. The system of claim 7, wherein saidmedia capture comprises one or more of a virtual reality display, avirtual reality presentation, a virtual reality recording, an augmentedreality display, an augmented reality presentation, and an augmentedreality recording.
 11. The system of claim 2, wherein said computer isfurther configured to obtain one or more additional sensor valuesassociated with one or more of an orientation, position, velocity,acceleration, angular velocity, angular acceleration, electromagneticfield, temperature, humidity, wind, pressure, elevation, light, sound,and heart rate from a second sensor or second computer or from aplurality of other sensors or other computers to perform said confirmsaid event.
 12. The system of claim 2, wherein said computer is furtherconfigured to obtain one or more additional sensor values from a secondsensor to perform said confirm said event, wherein said second sensorcomprises one or more of a video camera, a light gate, a radar, or aninertial sensor.
 13. The system of claim 2, wherein said computer isfurther configured to determine one or more tags for said event byanalyzing one or more of said data, said event data, and said one ormore of said text, audio, image, and video from said server; and,publish said one or more tags for said event to said social media siteor said social media service; and, said one or more tags represent oneor more of an activity type of said event; a location of said event; atimestamp of said event; a stage of an activity associated with saidevent; a player identity associated with said event; a performance levelassociated with said event; and, a scoring result associated with saidevent.
 14. The system of claim 2, wherein said suggestion comprisesrecommended friends.
 15. The system of claim 2, wherein said alertcomprises an indication of an emergency.
 16. The system of claim 2,wherein said suggestion comprises an indication of fake news.
 17. Thesystem of claim 2, wherein said computer is further configured to obtaina plurality of instances of said data or said event data or both saiddata and said event data from a plurality of other sensors or othercomputers; correlate said plurality of instances of said data or saidevent data or both said data and said event data based on a time and alocation of each of said plurality of instances of said data or saidevent data or both said data and said event data; and, perform saidconfirm said event when the number of said plurality of instances ofsaid data or said event data or both said data and said event dataoccurring within a time range and a location range exceeds an eventcount threshold, or, an aggregate metric calculated from said pluralityof instances of said data or said event data or both said data and saidevent data for the plurality of instances of said data or said eventdata or both said data and said event data occurring within said timerange and said location range exceeds an aggregate metric threshold. 18.The system of claim 2, wherein said computer is further configured todetect a first value from said one or more values having a firstthreshold value and detect a second value from said one or more valueshaving a second threshold value within a time window; signify aprospective event; compare said prospective event to a characteristicsignal associated with a typical event and eliminate any false positiveevents; signify a valid event if said prospective event is not a falsepositive event; and, save said valid event in said memory comprisinginformation within an event time window as said data.
 19. The system ofclaim 2, wherein said computer is further configured to obtain at leastone video from one or more of a camera or smart glasses associated withsaid user, said computer, said mobile device, said server, and anotherserver; obtain at least one video start time and at least one video stoptime associated with said at least one video; and, synchronize saidevent data with said at least one video based on said event start timeand said event stop time, and at least one time associated with said atleast one video, to create at least one synchronized event videocomprising both of said event data that occurs during a timespan fromsaid event start time to said event stop time; and, said at least onevideo captured during said timespan from said event start time to saidevent stop time.
 20. The system of claim 19, wherein said computer isfurther configured to accept a metric or one or more tags associatedwith said at least one synchronized event video; accept selectioncriteria for said metric or said one or more tags; determine a matchingset of synchronized event videos that have a value or values associatedwith said metric or with said one or more tags that pass said selectioncriteria; and, display said matching set of synchronized event videos orcorresponding thumbnails thereof along with said value or valuesassociated with said metric or with said one or more tags for each ofsaid matching set of synchronized event videos or said correspondingthumbnails.
 21. A sensor and social media integration system comprising:a computer comprising a computer memory; and, a first communicationinterface configured to obtain data from at least one sensor configuredto measure a property of an object; wherein said computer is coupledwith said computer memory and is coupled with said first communicationinterface, wherein said computer is configured to receive said data fromsaid first communication interface and analyze said data and recognizean event within said data to determine event data, or said event datafrom said first communication interface, or both said data and saidevent data from said first communication interface; store said eventdata in said computer memory; obtain an event start time and an eventstop time from said event data; confirm said event recognized by saidcomputer or obtained from said first communication interface for aparticular location and time by analyzing one or more of text, audio,image, and video from a server to create a confirmed event, wherein saidone or more of said text, audio, image, and video comprise one or moreof email messages, voice calls, voicemails, audio recordings, videocalls, video messages, video recordings, text messages, chat messages,postings on social media sites, postings on blogs, and postings onwikis; and, publish said confirmed event.
 22. The system of claim 21,wherein said object comprises one or more of a person, a group ofpeople, a body part of said person or of said group of people, a food, adrink, a plant, an animal, a piece of equipment, a machine, anautomobile, a vehicle, an engine, a building, a room, an area, and abody of water.
 23. The system of claim 21, wherein said propertycomprises one or more of acoustic pressure, acoustic power, acousticfrequency, sound, vibration, seismic activity, air flow, fluid flow,mass flow, oxygen level, hydrogen level, ozone level, pH, smoke level,carbon dioxide level, carbon monoxide level, chemical composition,ionization level, chemical reaction rate, radiation level, charge,electric current, electric potential, resistance, conductance,capacitance, inductance, impedance, electromagnetic field,electromagnetic frequency, wavelength, Doppler shift, light level,particle count, photon count, amplitude, temperature, moisture,humidity, barometric pressure, pollution level, precipitation level,tide level, wind velocity, mass, weight, density, position, depth,altitude, displacement, proximity, presence, orientation, angle,inclination, tilt, shock, strain, mileage, velocity, speed, angularvelocity, acceleration, angular acceleration, force, torque, momentum,revolutions per minute, heart rate, blood pressure, body temperature,blood composition, body fluid composition, tissue composition, oxygensaturation, and respiration rate.
 24. The system of claim 1, whereinsaid least one sensor configured to measure said property of said objectwherein said one or more values are associated with a chemical value.